Tech Talks Daily

Neil C. Hughes

If every company is now a tech company and digital transformation is a journey rather than a destination, how do you keep up with the relentless pace of technological change?

Every day, Tech Talks Daily brings you insights from the brightest minds in tech, business, and innovation, breaking down complex ideas into clear, actionable takeaways.

Hosted by Neil C. Hughes, Tech Talks Daily explores how emerging technologies such as AI, cybersecurity, cloud computing, fintech, quantum computing, Web3, and more are shaping industries and solving real-world challenges in modern businesses.

Through candid conversations with industry leaders, CEOs, Fortune 500 executives, startup founders, and even the occasional celebrity, Tech Talks Daily uncovers the trends driving digital transformation and the strategies behind successful tech adoption. But this isn't just about buzzwords.

We go beyond the hype to demystify the biggest tech trends and determine their real-world impact. From cybersecurity and blockchain to AI sovereignty, robotics, and post-quantum cryptography, we explore the measurable difference these innovations can make.

Whether improving security, enhancing customer experiences, or driving business growth, we also investigate the ROI of cutting-edge tech projects, asking the tough questions about what works, what doesn't, and how businesses can maximize their investments.

Whether you're a business leader, IT professional, or simply curious about technology's role in our lives, you'll find engaging discussions that challenge perspectives, share diverse viewpoints, and spark new ideas.

New episodes are released daily, 365 days a year, breaking down complex ideas into clear, actionable takeaways around technology and the future of business.

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#3752
Today · 27 min
Who Controls Your Data in a Sovereign AI World With NetApp
Could your board explain where the organization's most valuable data resides, who can control it, and how quickly it could be recovered In this episode of Tech Talks Daily, I speak with Adam Gale, CTO for Cybersecurity and Artificial Intelligence at NetApp, about why digital sovereignty is moving from compliance teams into boardroom discussions. Adam argues that data has measurable business value, so decisions about its location, ownership, protection, and recoverability should be treated with the same seriousness as other financial risks. We discuss how the US CLOUD Act, Europe's sovereignty agenda, and wider geopolitical tensions are changing cloud decisions. Adam explains that sovereignty extends beyond physical data location. Hardware supply chains, software development, labor, access, operational control, and recovery all influence the level of sovereignty an organization may require. His advice is to treat sovereignty as a scale rather than impose the same controls on every workload. The conversation also turns to sovereign AI. Adam describes why businesses and governments want greater control over training data, intellectual property, model activity, and audit records. He explains how poisoned data can produce plausible but unsafe outputs, and why controlled AI environments, traceable logs, and immutable records matter when systems begin making consequential decisions. We then connect data visibility with business value. Adam shares how uncertainty over data location can affect a company acquisition and explains why insurers increasingly ask organizations to prove how information is protected and recovered. He also describes the growing role of behavior analytics, automated responses, immutable storage, and storage-level ransomware protection in limiting damage and restoring trusted data. If digital sovereignty begins with responsibility, control, and security, who owns those decisions in your organization today? Listen to the conversation and share your thoughts with me.
#3751
Yesterday · 34 min
Moving Enterprise AI From Pilot to Production With Stelia
Why do promising AI pilots struggle when they become services that real customers depend on? In this episode of Tech Talks Daily, I'm joined by David Hughes, Chief Technology Officer at Stelia, to talk about the engineering decisions behind enterprise AI deployment. Dave's responsibilities cover networking, cloud infrastructure, and customer-facing products, giving him a view of what happens when an application meets the physical systems expected to support it. We begin with a question that can get lost in the excitement around a successful demonstration. What problem was the system designed to solve, and what happens when the number of users changes? Dave describes reviewing a proof of concept that had worked for one external customer, only to identify a messaging component he believed would become a bottleneck under heavier demand. His lesson is to understand the intended workload before treating a working prototype as a production service. The discussion also offers a balanced view of cloud computing. Dave praises elastic resources and programmatic control, while arguing that demanding AI workloads require teams to understand how storage, networking, and compute interact. A fast network cannot compensate for storage that cannot feed it. His steamship analogy makes the point familiar: the engine needs a steady supply of fuel if it is going to maintain its speed. We talk about lessons from high performance computing, Dave's experience at Genesis Cloud, and the practical constraints of finding data center space and power. He explains why location and available capacity can influence architecture before a team even starts comparing GPUs. For business leaders, the conversation becomes especially useful when we turn to measurement. Dave describes assessing internal model services through their effect on spending and judging customer projects by their output. His hotel occupancy analogy raises a question for anyone approving an infrastructure budget: how much of the asset's capacity will actually be used? There is also a tension between engineering discipline and experimentation. Dave describes starting an internal inference service with existing servers and GPUs, then expanding it as demand grew. Proving that people wanted the service helped establish a case for a fuller investment. The lesson is to distinguish a useful experiment from evidence that a system is ready for wider demand. We close with the Stelia newsroom and its engineer-written technical content. The supplied guest notes also introduce Stelia AI OS, described by the company as a full-stack operating system for enterprise AI. The interview focuses on deployment principles rather than a detailed assessment of that platform. Where does your organization encounter the biggest obstacle when moving AI into production, and what evidence helps you decide where to invest next? Please share your thoughts.
#3749
Saturday · 27 min
Reducing Hospitality Friction Without Losing Human Service With Aven Hospitality
Can a hotel modernize its technology without turning hospitality into another automated transaction? In this episode of Tech Talks Daily, I speak with Mark Hollyhead, Chief Transformation Officer at Aven Hospitality, about the systems behind thousands of hotels and the operational decisions that affect both employees and guests. Mark brings experience from British Airways, Heathrow Airport, Expedia, and travel management before joining the company carved out from Sabre with support from TPG. His argument begins with a distinction that many technology programs miss. Guests rarely remember the property management system, API, or data pipeline behind a stay. They remember whether somebody knew their name, solved a problem, explained when the room would be ready, or prevented them from repeating information after a long journey. Technology matters because it can remove the administrative work surrounding those moments. Mark wants frontline employees to have time to be hospitable, especially when a situation requires empathy or judgment. That means looking beyond the promise of convenience and asking whether a system gives staff better information, reduces repeated work, and leaves them free to focus on the person in front of them. Mark shares an example from a hotel he visits regularly in Dallas. Different employees recognized a few preferences from previous stays, including his connection with Seattle, a request for two bottles of water, and a preference for a corner room. None of those details required the hotel to know everything about him. They gave the employee enough context to make the interaction feel personal. The example also illustrates the boundary between thoughtful personalization and an experience that feels intrusive. Mark's test is whether the customer would be surprised by what the company knows. If the guest begins wondering how the information was obtained, trust has already weakened. Hotels hold identity, payment, preference, and location data, including the place where somebody is sleeping, so that responsibility deserves careful treatment. For technology investment, Mark begins with friction rather than a product category. He looks for employees rekeying information, building spreadsheets because systems do not communicate, switching among screens, and developing improvised techniques to complete ordinary work. Across one property, five unnecessary minutes may appear minor. Across thousands of employees and repeated tasks, those minutes become expensive. This approach changes how transformation is measured. Mark says another login, dashboard, or screen usually indicates that software has been added without changing the underlying work. He asks what has disappeared after a release. Fewer clicks, calls, handoffs, and repeated explanations provide clearer evidence than the launch itself. Hotels also depend on a large network of systems covering reservations, property management, payments, distribution, loyalty, revenue management, customer relationships, and housekeeping. When definitions differ across those systems, employees become the integration layer. Data quality problems then appear at the exact moment a guest expects a simple answer. Mark believes the arrival of agentic commerce will make trusted and consistent information even more important. Travelers who have spent decades filling out search forms may increasingly describe what they need through a conversation. The AI agent will expect accurate room attributes, availability, rates, preferences, and policies from connected sources. Conflicting answers will damage confidence quickly. The episode also challenges the familiar debate between digital and human service. Mark argues that people do not wake up choosing one or the other. They want an easy experience suited to the circumstances. A traveler arriving after a normal flight may prefer to go directly to the room. A traveler stranded in a snowstorm is unlikely to appreciate a chatbot repeating that it understands their frustration. His operating principle is to automate predictable work and make it simple. When a situation becomes ambiguous, emotional, or unusual, a person should be able to step in with the context already available. The handoff matters as much as the automation because the employee should not force the guest to repeat the entire story. This has implications for frontline adoption. Mark rejects the assumption that resistance means employees dislike change. People often dislike bad change, especially when a new system makes the job harder and the response is a lengthy training session explaining why it should feel better. Aven involves frontline staff earlier so their knowledge can influence workflows before technology is introduced. AI can also help prioritize work by showing employees the next action that deserves attention. That does not eliminate human judgment. It can give staff the information and authority required to resolve the most important problem without searching through several systems while an increasingly frustrated guest waits. The opportunity is a hospitality operation where technology works quietly, reliable information travels with the guest, and employees have greater capacity to create memorable moments. The risk is adding automation to disconnected processes, collecting personal information without a clear purpose, or mistaking another launch for an improved experience. Where would removing five minutes of friction create the greatest difference for your employees and customers? Listen to the episode and share your thoughts.
#3748
Friday · 34 min
Building Trust in Autonomous IT With LogicMonitor
When should an AI system be allowed to fix an IT problem without waiting for a human to approve the action? In this episode of Tech Talks Daily, I speak with Matt Tuson, General Manager for Europe, the Middle East, and Africa at LogicMonitor, about the practical path from reactive IT operations toward autonomous IT. The promise is attractive: fewer repetitive tasks, faster incident resolution, less disruption, and systems that can correct familiar problems before users feel the impact. The difficult part is deciding when the available data, controls, and evidence are strong enough to trust an automated action. Matt argues that observability is taking on a different responsibility. It once helped engineers answer what happened and why. In an autonomous environment, it must also give AI enough connected context to recommend or execute what should happen next. That means understanding how infrastructure, networks, cloud services, applications, and other operational signals relate to one another. A fast decision based on an incomplete view can resolve the wrong symptom, create duplicate incidents, or make the original problem worse. Trust therefore begins with the information supplied to the system. Many IT environments contain separate monitoring and management tools introduced by different teams to solve specific problems. Those specialist products may continue to do useful work, but their signals often remain disconnected. Matt describes a business combining nine organizations, each bringing its own tools and operational practices. The challenge was not simply the number of products. It was the inability to connect their outputs into a reliable view of what was happening across the combined environment. This matters because AI can process poor information as quickly as good information. Matt warns that adding autonomous remediation to incomplete, duplicated, outdated, or poorly contextualized data risks creating automated confusion. False positives multiply, root causes are missed, and teams repeatedly address symptoms while the underlying fault remains. His advice is to start with the business outcome, identify the information needed to support that outcome, and improve the quality and consistency of the data before increasing AI authority. We also discuss how leaders can decide which actions belong with AI and which should remain under human control. Matt rejects the idea that organizations must choose between full autonomy and complete human approval. He recommends graduated levels of authority based on the action, confidence in the recommendation, business impact, and whether the change can be reversed easily. Ticket enrichment, alert correlation, and routine remediation may offer lower-risk starting points. A change affecting a major service should receive closer human oversight until the system has established a reliable record. That creates a practical model for earning trust. If an AI recommendation repeatedly matches the action an experienced engineer would have taken, the organization gains evidence that it may be safe to automate that decision. The process can move gradually through support levels and operational complexity, with explainability, governance, and audit records maintained throughout. Human judgment remains where accountability and business consequences demand it. Matt also addresses the difference between companies making progress with AI and those stuck in pilot purgatory. The stronger performers begin with an operational problem, such as reducing incidents, improving uptime, or accelerating resolution. They invest early in data quality, visibility, collaboration, and governance. AI becomes part of an existing workflow with a defined path from insight to action, rather than a detached experiment looking for a reason to exist. For IT leaders who want greater autonomy but do not yet trust their environment, Matt's starting point is simple: establish broad visibility, improve operational data, connect isolated sources, and define the actions AI can take independently. Teams must also be able to understand why a system reached its conclusion and inspect what it did afterward. Autonomy should grow through repeated evidence, not through hope or one large leap. There is a commercial reason to get this right. Faster resolution can reduce the cost of service, protect customer experience, and limit the business impact of downtime. There is also a human benefit. Engineers spend less time sorting duplicate alerts or joining war rooms to prove which team was innocent. They can concentrate on decisions that require experience, judgment, and knowledge of the business. As observability starts supplying evidence to machines as well as people, what controls would make you comfortable allowing AI to take action inside your IT environment? Listen to the episode and share your thoughts.
#3747
Thursday · 38 min
Moving AI From Pilot to Production With Broadridge
What has to change before an AI pilot can become part of a regulated financial workflow? In this episode, I speak with Tom Carey, President of Broadridge's Global Technology and Operations Business, about the work required to move AI beyond personal productivity tools and prototypes. Tom brings an unusual perspective to the conversation. He studied AI in the 1990s, long before current computing power and language models made commercial deployment practical, and now works where financial market infrastructure, technology and operations meet. Tom describes four levels of AI productivity. The first helps an individual complete a task. The second turns successful personal patterns into tools that other employees can use. The third places agents inside production workflows while a person verifies the result. The fourth allows agents to operate without constant human review, but only within a controlled environment with monitoring, policies and evidence that the system is behaving as expected. One of the strongest lessons from our conversation is that AI can force an organization to examine a process it may have accepted for years. Tom says Broadridge removed 30 percent of the email traffic entering one operational group after finding duplicates, messages that required no response and work that should not have existed. His own estimate is that around half the benefit of an AI program may come from simplifying the current process before advanced models are applied. That is an important counterweight to the assumption that every inefficiency needs a language model. We also discuss the economics of enterprise AI. Tom compares current token-cost surprises with the early cloud projects that moved existing workloads without redesigning them for a different computing model. Broadridge baselines costs, tests use cases through its operations business and monitors token consumption. He shares one example in which a team's default configuration automatically moved to a newer and costlier model, showing why model selection and configuration belong in the operating discipline around AI. For financial institutions choosing their first agentic workflow, Tom recommends high-volume, rules-based work with known inputs and outputs. A person or an automated test should be able to verify the result. He also favors several smaller agents over one large agent responsible for an entire process because individual steps are easier to inspect, replace and stop when something goes wrong. As models become widely available, Tom believes business advantage will increasingly depend on the proprietary data, platforms, workflows and governance surrounding them. Does your organization have the process discipline, cost visibility and control framework required to put AI into production? Listen to the episode and share your thoughts with me. Useful Links Executive: Tom Carey, President of Broadridge's Global Technology & Operations business Broadridge Deploys Agentic AI at Institutional Scale Across Capital Markets and Wealth Operations (May 11, 2026) Broadridge Joins Anthropic's Project Glasswing (June 17, 2026) GenAI Delivering Now, Tokenization Is Next: Financial Services Enters Period of Accelerating Transformation, Landmark Broadridge Study Finds (February 25, 2026) LTX Launches Agentic AI in BondGPT, Turning AI Insights into Trading Action (June 16, 2026) Broadridge Invests in DeepSee, Further Harnessing Agentic AI to Transform Post-trade Operations (January 8, 2026)
#3746
Wednesday · 34 min
Measuring Workplace AI by Employee Outcomes With DXC Technology
What if the most useful measure of workplace technology was not the number of tickets closed, but the amount of productive time returned to employees? In this episode of Tech Talks Daily, I speak with Kelly Candler, Global Offering Lead for Workplace and Business Process Services at DXC Technology, about moving digital workplace services away from activity metrics and toward employee and business outcomes. Kelly has worked on both sides of the managed services relationship. Before joining DXC, she was a customer of the company and several of its competitors, giving her a practical view of what large enterprises expect from workplace technology and where support models continue to disappoint. Kelly begins with a simple distinction. Traditional IT reporting often focuses on ticket volumes, response times, and calls answered. Employees care about whether they can do their jobs, how much time they lose, and how quickly they can return to productive work. When the outcome becomes the starting point, support is judged by its impact on the employee rather than the amount of activity generated behind the scenes. That matters because many companies already have crowded workplace technology estates. Years of investment have produced overlapping tools, legacy applications, automation, device platforms, and service channels. Adding another AI product can increase cost and confusion if it replaces nothing and connects to little. Kelly argues that organizations should understand the technology they already own, identify the employee outcomes they want, and use orchestration to connect those investments rather than discarding them automatically. DXC positions its Workplace Services offering as a people-centered, AI-native service enabled by DXC OASIS, its orchestration platform for Human+ agentic AI workflows. In the interview, Kelly describes Human+ as a partnership in which AI handles repetitive work while people retain creativity, judgment, and accountability. The intention is to make support more proactive, provide employees with help through familiar channels, and allow experienced teams to concentrate on work that requires human knowledge. Device performance provides a practical example. In a reactive model, employees discover that a laptop has become slow, unstable, or unusable after productivity has already been lost. Kelly explains that operational data and automation can identify patterns such as declining battery health, storage problems, and application conflicts before the employee experiences a failure. The system may resolve the issue automatically or arrange a replacement before work is interrupted. Kelly says DXC performs over 82 million proactive checks and remediations annually across over one million managed endpoints. These are company-reported operating figures, but they illustrate the scale at which preventative workplace support is being applied. She expects agentic AI to extend that model by learning from operational data and improving the speed and scope of proactive action. We also examine DXC's reported outcome figures. The company says Workplace Services can produce a 40 percent reduction in operational complexity, 60 percent fewer service desk calls, resolve 50 percent of device issues before employees notice, and return over 15 hours of productivity to each employee every month. Kelly explains that the productivity calculation considers incident resolution, device availability, PC performance, and onboarding. The actual result will depend on the employee persona, environment, baseline, and method of measurement, so leaders should examine how each figure was calculated before applying it to their own workforce. The discussion then moves from technology to adoption. Kelly says organizations often know where to run an AI proof of concept but struggle to turn the result into a production capability. DXC's response is an Exponential Blueprint that assesses the environment, maturity, culture, and potential value areas, then helps customers move from a smaller test toward wider use. The wider lesson is that a successful pilot needs a defined route into existing operations, ownership, governance, and measurement. Governance becomes especially important when Human+ workflows begin making decisions at scale. Kelly notes that human service desk employees already make mistakes, so the standard for AI should focus on risk tolerance, oversight, and what happens when an error occurs rather than assuming perfection. DXC has introduced an AI code of conduct alongside its employee code of conduct. Kelly says humans remain accountable for the actions taken by AI agents, much like the accountable role in a RACI model. Trust also depends on how leaders explain the changes to employees. Automation can raise concerns about surveillance and job loss. Kelly frames the opportunity as removing repetitive work so people can concentrate on decisions, creativity, and business outcomes. That promise will only be credible if employees understand how the technology is used, what remains under human control, how performance is measured, and where they can challenge an automated decision. For CEOs deciding where to begin, Kelly recommends support and device management because both affect nearly every employee and can produce measurable results. She also suggests moving toward experience level agreements that connect workplace performance with employee outcomes. The longer-term value comes from understanding the broader environment in which employees work and improving how its parts operate together. If workplace AI is intended to give people time back, should organizations retire ticket volume as their headline measure and report the employee impact instead? Listen to the episode and share your thoughts.
#3746
Tuesday · 24 min
Bringing Your Own AI Agents Into Customer Service With Front
What happens when the AI agent a business wants to use was not built by the customer service platform where its employees already work? In this episode of Tech Talks Daily, I speak with Dan O'Connell, CEO of Front, about Bring Your Own Agent and the growing demand for companies to combine native agents, third-party systems, and agents they build themselves. The conversation moves beyond the question of which model is best and focuses on the operating choices that determine whether those agents can work safely and usefully around customers. Dan describes Front as a customer operations platform that brings support and customer success teams together around customer problems. Front has its own native agents. Autopilot can complete tasks from end to end, while Copilot assists employees with escalations, handoffs, and daily service work. Yet Dan does not expect every customer to use only the agents supplied by one platform. Some businesses are creating agents for a particular workflow. Others want greater control over cost, speed, or model complexity. A straightforward task may suit a faster and less expensive model, while a sensitive or difficult request may demand a different system. Dan believes companies will want the freedom to connect those choices with the platform where their employees and customer conversations already live. Front says an early announcement of this approach attracted over 1,000 signups. That figure is company-reported, but it points to a practical issue many technology leaders are now facing. AI purchasing is no longer one decision made for the whole company. Different teams may select different models and agents, while employees still need a consistent place to coordinate the work. Choice alone does not produce a coherent customer experience. An agent needs the history of the customer relationship, the current conversation, the actions already taken, and a clear understanding of what should happen next. It also needs to recognize when a request falls outside its skills and should be transferred to another agent or a person. That handoff is where many customer experiences fail. People become frustrated when they have to explain the same issue again after moving between channels, departments, or systems. Adding agents can increase that risk if context does not travel with the work. Dan argues that the platform must preserve the information surrounding each transfer so the next participant can continue rather than restart the conversation. The right boundary between people and agents depends on the business and the experience it wants to provide. Dan contrasts repeatable e-commerce requests, such as order tracking, refunds, or returns, with customer conversations involving health products. An agent may handle many routine retail questions from the first contact. A health-related interaction may need empathy, judgment, or a person involved earlier, even when automation could technically complete the task. This makes automation a design choice rather than a contest to remove the largest possible amount of human work. A company must decide whether an agent should be the first contact, whether an employee should approve an action, and where people should manage escalations or coordinate several agents. The answer may vary by customer, channel, task, or level of risk. Dan describes autonomy as a dial. A team can begin with an agent making recommendations while a person approves the final action. It can then increase autonomy when testing and operating evidence support that decision. This approach gives cautious organizations a way to introduce agents without handing over every task at once. Observability and governance are part of that process. Managers need audit logs showing what an agent did and why it reached a recommendation. They also need testing that shows how the system responds to representative scenarios. If something goes wrong, Dan is clear that people still own the outcome. Using AI does not remove responsibility from the company deploying it. Measurement should focus on the customer result. Resolution and deflection rates can look positive even when an interaction creates frustration or extra work. Dan recommends examining whether the customer's problem was solved and whether the experience was satisfactory, regardless of whether a person or an agent performed the work. Front uses inferred customer satisfaction across both human and AI interactions as one way to compare those outcomes. The interview also addresses the tension between flexibility and control. Connecting several agents, models, and vendors can create another fragmented technology estate. Dan's answer is not unlimited autonomy. It is shared context, defined permissions, human approval where needed, audit trails, testing, and the ability to change the level of automation. Looking toward 2027, Dan expects companies to select fewer core platforms while allowing several agents to operate through them. He also expects boundaries between support, customer success, and account management to continue becoming less distinct. For customers, those internal structures matter less than receiving a consistent answer from a company that remembers the relationship. The conversation leaves leaders with a practical question. If a business gives teams the freedom to choose the agents that fit their work, can it also provide the common context and accountability required to protect the customer experience? Would your organization benefit from bringing its own agents into a shared customer operations platform, or would the added choice create another coordination problem? Listen to the episode and share your thoughts.
#3738
Tuesday · 23 min
Testing Cyber Resilience Before an Incident With SolarWinds
What does cyber resilience look like when leaders must make a costly decision before anyone fully understands the incident? In this episode of Tech Talks Daily, I speak with Justin Henkel, CISO at SolarWinds, about moving the security conversation beyond prevention and preparing the wider business to respond, recover, and communicate under pressure. Justin brings 22 years of Air Force experience across intelligence, cyber operations, and special operations, alongside leadership roles in the public and private sectors. His intelligence background shapes a simple but demanding approach to uncertainty. Technical teams may need time to establish exactly what happened, but business decisions cannot always wait for a complete answer. Leaders need to understand what is known, what remains uncertain, which customers could be affected, and what action limits the potential damage while the investigation continues. Justin illustrates that tension through an incident involving a database that returned larger responses than expected, including information connected with other customers. The team did not initially know the cause or full extent. His recommendation was to take the affected product offline while the company investigated. Sales and marketing colleagues were understandably concerned about revenue and reputation, but Justin framed the decision in commercial terms. A temporary loss measured in millions was preferable to allowing one problem to become hundreds of problems with consequences measured in billions. The incident was ultimately connected with the timing of a database update and a system that failed open rather than failing safely. The example shows why the CISO's job has expanded beyond explaining technical controls. Security leaders must help executives compare immediate commercial pain with the possible cost of leaving an uncertain situation untouched.
#3744
October 5 · 28 min
Stopping Shadow AI Without Slowing Employees With Liminal
Can an organization control employee AI use without making the approved tools so restrictive that people simply work around them? In this episode of Tech Talks Daily, I speak with Steven Walchek, Co-Founder and CEO of Liminal, about shadow AI, enterprise governance, data privacy, and the growing tension between employee productivity and corporate security. Steven's career includes leadership roles at FIS and AWS, along with involvement in three successful exits. His experience has given him a close view of how major technology adoption cycles begin inside companies, often before leadership has developed the policies, budgets, and controls needed to manage them. The story behind Liminal began with an intensive period of customer discovery. Steven and his co-founder spoke with 100 prospective customers in 90 days. Across large and small businesses, they repeatedly heard concerns about what would happen to company data after employees submitted it to generative AI providers. That concern has grown as AI tools have spread through the workplace. Steven describes two common responses from CIOs. Some permit employees to use almost any AI product, despite limited visibility into licensing terms, data retention, model training, or regulatory exposure. Others attempt to prohibit AI use completely and assume a written policy will stop employees from accessing these services. Neither response accounts for how people behave when they believe a tool can help them work faster. An employee may use a personal account, take a photograph of a screen, or transfer information onto another device. The company has technically established a policy, but its security team may now have even less visibility into what is happening. Steven compares this with the early adoption of cloud computing. Developers and business teams could purchase services with a credit card, while finance leaders later discovered rapidly growing AWS bills. Cloud adoption created shadow IT because people had access to useful technology before company controls caught up. Generative AI is producing a similar pattern at greater speed. We discuss why aggressive security warnings can also produce unintended behavior. If an approved platform repeatedly frightens or reprimands employees for submitting information, they may move to an unapproved tool that creates less friction. From the employee's perspective, the objective is usually straightforward: complete the work and produce a good result. Steven argues that companies need an approach that gives employees a familiar AI experience while providing security teams with governance, model administration, data protection, observability, and an audit trail. He explains how Liminal attempts to combine access to several AI models with controls operating behind the user experience. We also discuss why listening to employees matters after deployment. Steven shares how customer feedback led Liminal's development team to change a spreadsheet feature within 24 hours. For him, that responsiveness helps businesses introduce governance without forcing people to choose between the approved system and the tool they believe can do the job. Should enterprise AI governance begin with restrictions, or with a better understanding of what employees are trying to accomplish? Listen to the conversation and share your experience.
#3713
October 4 · 20 min
Making Better Marketing Decisions With Braze AI
How can marketers make better decisions for individual customers without spending their working lives designing, running, and maintaining separate tests? Recorded at Braze Forge 2026 at the Fontainebleau in Las Vegas, this episode of Tech Talks Daily features my conversation with George Khachatryan, Head of AI Decisioning at Braze. George describes leading product management for AI Decisioning Studio and shares the story behind OfferFit, the company he cofounded before it became part of Braze. We begin with the practical limits of segmentation and A/B testing. George explains that smaller customer segments can make it harder to collect enough evidence for a useful result, while each new creative option introduces further work. His explanation of reinforcement learning offers a different approach. The marketer defines the objective and the options available to the system, which then experiments, observes the results, and adjusts its decisions. We discuss the distinction between Decisioning Studio Pro and the newly announced Decisioning Studio Go. George describes Pro as offering flexibility around success metrics and custom data, with data science support required during implementation. Go is designed as a self-service option using data generated within Braze. At the time of recording, he says Go supports email and optimizes click activity with machine clicks filtered out. Marketers choose the journey, creative options, subject lines, calls to action, available timings, frequencies, and guardrails. The limits are as useful as the possibilities. George recommends a baseline of at least a few thousand clicks per month for a journey using Go, so the model has enough information to learn. He also acknowledges that maximizing clicks will not always maximize conversions. We discuss why those objectives need to be assessed separately, rather than treating improved interaction metrics as proof of additional sales. George explains how the system can learn from similarities between creative variants and describes daily model retraining as a way to adapt to changing behavior. We also talk about reporting against a business-as-usual control group. He distinguishes the performance reporting available at the time of recording from deeper explanations of why a model made a particular choice, which he describes as an area of ongoing development. One of the most memorable parts of the conversation concerns customer trust. George recounts arriving with his family for an apartment viewing arranged by an AI assistant, only to discover that no appointment had been booked. His point is that speed and responsiveness lose their value when a company refuses responsibility for the actions of its AI. Transparency and ownership of the customer experience still require human judgment. We finish with George's advice on readiness, including experience with manual testing, measurement, and customer data. His broader comments about data infrastructure should be considered separately from his description of Go's use of native Braze data. Which marketing decision would you automate first, and how would you check that it was improving the outcome you actually care about? I'd love you to share your thoughts.
#3742
October 3 · 17 min
Forge26: AI Marketing With Human Creativity With Braze
How can marketers use AI to improve the customer experience without filling every channel with increasingly similar content? Recorded at Braze Forge 2026 in Las Vegas, this episode of Tech Talks Daily features my conversation with Christy Poulos, VP of Product Marketing at Braze. We discuss the practical choices behind AI marketing, from deciding what a campaign should achieve to keeping creative work, compliance, and customer relationships under human direction. Christy describes Forge as an opportunity for marketers to talk about their craft and the problems they face every day. Technology forms part of that conversation, but her starting point is the work itself. Teams are being asked to adopt AI while also demonstrating a useful return. Her advice is to connect the tools they choose with specific goals for the brand, the team, and the wider business. Producing additional content offers little reassurance if nobody has agreed what success looks like. We discuss Agentic Standards and the less glamorous work of checking campaigns against rules. Drawing on her experience marketing a regulated product, Christy explains why automating repetitive checks could give teams greater confidence in their output. The marketer still sets the boundaries. Her argument is that campaigns should operate within those boundaries whether a human or an agent prepares them. The interview presents the intended value of these capabilities, rather than independent evidence that they remove compliance risk. Another part of the conversation concerns where marketers actually want to work. Operator Connect introduces ways to connect AI assistants and other working environments with Braze. Christy discusses campaign briefs created in Claude and the growing interest in collaboration through Slack. She also makes a clear case for retaining the traditional product interface, where teams can review a campaign in context, check standards, and launch it. Some businesses are interested in these connections, while others are still developing their AI skills or learning what the tools can do. The question of creative quality runs through the interview. Christy believes customers will recognize generic AI content and that this could weaken their relationship with a brand. Her view is that marketers who continue to bring their own creativity and work alongside AI will produce stronger results. We also discuss conversational agents and the possibility of customer interactions that allow people to respond and feel heard, rather than simply receive another broadcast message. For teams wondering where to begin, Christy recommends Content Optimizer and email content testing as a first step. She describes the possibility of testing many variants, then progressing to Decisioning Studio Go to consider send times and channels. These are her recommendations for adoption, not measured results from a customer case study. Later, she discusses how insights from decisioning products could help marketers contribute to product strategy and business planning. One of the most memorable examples arrives toward the end, when Christy describes a ride hailing platform in Venezuela using Braze to create an earthquake awareness system. The company is not named in the interview, but the story illustrates her broader point about customers finding applications that product teams had not anticipated. Where could AI make your marketing more useful to customers, and which decisions should remain with the people who understand them? I'd love you to share your thoughts.
#3741
October 2 · 23 min
Building Governed AI Across Distributed Data With Starburst
Can an enterprise move quickly with AI when the information it needs remains spread across business units, acquired companies, private data centers, multiple clouds, and systems governed by different privacy rules? In this episode , I speak with Justin Borgman, co-founder and CEO of Starburst, about the data architecture decisions now affecting how quickly businesses can turn AI investment into useful results. The conversation begins with a reality many established companies will recognize. Their technology estate reflects years of applications, acquisitions, regulatory demands, regional choices, and earlier infrastructure programs. Justin argues that this history becomes a constraint when AI teams need governed access to information quickly. A company created within the last year may design its data environment around AI from the beginning. A multinational enterprise rarely has that freedom. It must work with valuable data held across different locations while respecting security, privacy, and sovereignty requirements. The traditional response has been to centralize everything. Justin believes the single source of truth is often an impossible target rather than a finished destination. Drawing on his earlier experience at Teradata, he says even customers using a leading database continued to retain information elsewhere. New applications, company acquisitions, regulations, and changing business demands kept creating additional systems. His preferred approach is to accept that distributed data will remain part of enterprise life and build an architecture that can work across it. A federated data platform can query information where it lives while giving users a common point of access. This can reduce the time spent moving data before an analyst, executive, application, or AI agent can use it. Justin illustrates the problem through a large American bank with many lines of business and inherited data silos. Senior leaders could ask commercially important questions, but answering them required analytics teams to write queries, assemble dashboards, and combine information from several systems. The process could take weeks. He describes how connecting those sources and adding a natural-language interface can reduce that delay. Starburst calls its interface ADA. It allows a user to ask questions in conversational language while drawing on governed enterprise information and its business context. The company presents this as a way to shorten the path from question to insight without forcing every data set into one platform first. Business value remains harder to prove than technical access. Justin recommends connecting data projects with revenue growth, cost reduction, or risk management. He also points to usage evidence within data platforms. If a data product is accessed frequently by leaders or operational teams, and the cost of producing it is known, those signals can help a company evaluate whether the investment is serving a recurring need. Architecture economics also influence where workloads belong. Justin favors S3-compatible object storage and open formats such as Parquet and Apache Iceberg when companies assemble data in a lake architecture. His argument is that open storage can reduce cost while allowing customers to choose among query engines rather than binding the information to one provider. That advice does not mean every workload belongs in one format or location. Some data will remain in warehouses, operational databases, regional systems, and on-premises infrastructure. Federation can provide access across those environments, while open formats create additional choice for the information that can be consolidated economically. The human side of architecture receives equal attention. Justin says ownership, incentives, and internal politics affect data quality because centralized teams may lack the domain knowledge held by the business unit that produced the information. Treating data as a product gives an organization a way to assign responsibility for quality, maintenance, adoption, and feedback. Named ownership changes the conversation. A successful data product can be recognized and improved because people know who created it. A weak product can receive feedback from its internal users. Distributed control can also allow teams closest to the data to apply their knowledge while the wider company accesses it through common governance. Governance becomes especially important when AI agents can query enterprise information directly. Justin says access controls must operate beneath the agent rather than relying on the model to decide what a user should see. Row-level and column-level permissions, data masking, and query auditing can determine which records are available and provide a record of what the system retrieved. Security and economics are also contributing to renewed interest in running some AI workloads on-premises. Justin says businesses may prefer open-weight models on owned hardware when scale improves the economics or when confidential data cannot comfortably leave the corporate firewall. He expects cloud and on-premises capabilities to coexist rather than one replacing the other. For employees, conversational access could change the role of traditional business intelligence. Justin expects standard KPI dashboards to remain useful, but believes many custom reports and one-off dashboards could be replaced by interactive models that answer questions directly. Analysts and engineers may spend less time responding to requests and additional time improving trusted data products and governance. The opportunity is faster access to answers. The risk is allowing speed to outrun security, quality, or accountability. A federated architecture can connect distributed systems, but it still requires companies to know who owns the data, which users can access it, how results are audited, and whether the outcome supports revenue, cost, or risk goals. Should enterprises keep pursuing one central source of truth, or accept distributed data as a permanent condition and build governed AI around it? Listen to the episode and share your thoughts.
#3740
October 1 · 29 min
Measuring AI by Business Results Instead of Adoption With Domino Data Lab
How should a business measure AI success when employee adoption tells leaders very little about revenue, savings, risk, or better decisions? In this episode of Tech Talks Daily, I speak with Thomas Robinson, better known as T-Rob, who recently moved from Chief Operating Officer to CEO of Domino Data Lab. After ten years inside the company, he has seen enterprise AI move through several phases, from specialist data science projects to generative AI tools available across the workforce. T-Rob argues that businesses have become too focused on the technology itself. Generative AI has attracted attention because almost anyone can use it, but an individual productivity tool is very different from an AI system making decisions about mortgages, clinical trials, financial markets, or defense operations. As the potential value of a decision rises, so does the financial, regulatory, and operational risk. That is why T-Rob believes governance should be built alongside AI development rather than added after a system has been completed. He compares the process with constructing a building. Engineers do not wait until the work is finished before checking whether it has been designed and assembled correctly. Reviews happen throughout construction. Domino applies the same principle to AI through policy controls, production monitoring, tracing, and continued human oversight. We also discuss why companies should avoid beginning with a fashionable tool and searching for somewhere to use it. T-Rob recommends starting with the company's primary business measures and working backward. A pharmaceutical business may examine the number of promising therapies entering its pipeline, revenue, and risk. The appropriate AI system can then be designed around those outcomes. That system may combine large language models with computer vision, statistical models, rules, and company data. T-Rob believes the assumption that every business problem requires the latest frontier model can waste money and produce weaker results. People remain a major part of the equation. T-Rob has seen companies reduce headcount in anticipation of AI replacing employees before the technology was ready. He argues that domain experts become more valuable because they understand the business history, operating environment, exceptions, and consequences that a model may miss. The conversation also considers model independence and AI sovereignty. Many enterprises became dependent on a single cloud provider by building their own technology on proprietary services. T-Rob believes businesses should avoid repeating that decision with AI models. Open systems can allow companies to replace models as prices, capabilities, regulations, and operational needs change. For organizations handling sensitive intellectual property, sovereignty also raises questions about what information leaves the business when employees prompt external models. T-Rob describes the risk of enterprise knowledge being absorbed into future model development, even when information has been anonymized. Perhaps his strongest argument concerns measurement. He calls consumption and adoption terrible measures of success because they mainly reveal cost. Giving every employee an AI tool does not mean the entire company becomes proportionally more productive. Real return comes from improving the business processes that generate revenue, reduce expense, control risk, or support better decisions. Are businesses ready to stop measuring AI by logins and start measuring what it changes inside the company? Listen to the episode and share your thoughts.
#3739
September 30 · 24 min
Braze at Forge 2026: Where Should Human Judgment End and AI Decisioning Begin?
What happens when artificial intelligence moves beyond helping marketers create content and begins making decisions on their behalf? Recorded at Forge 2026 in Las Vegas, I speak with Astha Malik, Chief Business Officer at Braze, about how AI is changing customer engagement and what marketers should retain control over as more operational work is handed to software. Astha explains why the long-standing promise of genuine one-to-one personalization has been so difficult to deliver and why she believes AI can finally help brands move beyond broad segments toward individual decisioning. We discuss Decisioning Studio Go, where AI can optimize content, timing, and frequency for different customers, while marketers continue to define the objectives and brand boundaries within which the system operates. But greater automation creates new questions. If AI can generate more campaigns and messages, does marketing simply become noisier? Astha talks openly about the danger of "AI slop" and why using the same models and tools can make brands increasingly forgettable. We also discuss Agentic Standards and the idea of AI checking the work of other AI systems before campaigns reach customers. Astha argues that organizations need controls around agents in much the same way they already have quality processes around human teams. Our conversation also moves beyond marketing into the changing enterprise software interface. Operator Connect allows Braze capabilities to be accessed through environments such as ChatGPT, Claude, and Microsoft Copilot, raising questions about whether employees will increasingly interact with business systems through AI assistants rather than traditional applications. Finally, we examine how organizations can prove AI is creating measurable value, why some businesses remain trapped in experimentation, and Astha's advice for leaders overwhelmed by the pace of change. Her recommendation is simple: start experimenting rather than waiting for certainty. As AI takes on more decision-making and execution, which parts of marketing should remain firmly in human hands? Listen to the conversation and share your thoughts.
#3737
September 29 · 21 min
Turning Disposable Research Into Continuous Insight With Cint
What if every market research project could continue contributing to business decisions after its original question had been answered? In this episode of Tech Talks Daily, I'm joined by Phil Ahad, Managing Director of Data at Cint, to discuss why he believes companies should move away from disposable research. For decades, the familiar model has been straightforward. A business asks a question, commissions a study, receives the answer and begins again when the next question appears. Phil argues that this process wastes useful information and repeatedly asks people for details that may already be available. His alternative is an always-on human data engine that allows new studies to build on previous research. Existing responses can be combined with first-party information, third-party sources, transactional records and behavioral signals. Phil says this can help organizations answer new questions faster while reducing the burden placed on respondents. That burden matters because survey fatigue is often misunderstood. Phil does not believe people have stopped wanting to share opinions. The problem is the experience. Customers are repeatedly asked long batteries of familiar questions, often after everyday transactions, because the structure of data collection has changed remarkably little since paper surveys. If researchers already know much of the background, they can ask fewer questions and focus on the reasons behind a person's decision. We also examine synthetic data, a term Phil openly dislikes, and the growing use of AI personas or digital twins. At one end of the spectrum, a model might add 200 modeled responses to an 800-person study so researchers can work with a sample of 1,000. Phil says this extends an existing data set rather than creating genuinely new insight. At the other end, a company may create a digital representation of a person from survey responses, purchasing patterns, mobile activity and other signals, then ask that representation new questions. The opportunity is faster research with less repeated questioning. The risk is believing the model knows a person better than the evidence allows. Phil says the industry must test how much information is required to predict an answer with an acceptable level of confidence. He expects progress to come from repeated comparison and validation rather than a single certification method or technical shortcut. For business leaders, this makes transparency as important as speed. Before relying on AI-augmented research for a major decision, they need to understand where the original data came from, how modeled responses were created, how performance was tested and where human judgment remains involved. Phil also notes that strong decisions rarely rely on a single input. Companies bring together research, customer records, benchmarks and other sources before deciding what to do. Cint's ambition, as Phil describes it, is to turn recurring tracking studies into a continuing source of insight. He says roughly one million people pass through the company's ecosystem each day, giving Cint an asset that can be combined with increasingly accessible technology. The larger challenge is making useful sense of growing data volumes at business speed. Could continuous research help your organization ask people fewer, better questions, or would AI-generated responses introduce uncertainty that outweighs the speed gained? Listen to the episode and share your thoughts with me.
#3738
September 28 · 23 min
Security Posture at Machine Speed With Barracuda
What happens when attackers can discover and exploit a weakness faster than your organization can patch it? Recorded at Barracuda TechSummit 2026 in Alpbach, Austria, this conversation features Arve Kjoelen, CISO at Barracuda. Arve is responsible for protecting Barracuda's systems, environment, and code, which makes him the person answering the familiar question of who checks the checker. Our conversation begins with the collapse in response time. Security teams once had hours or days to investigate suspicious activity. Arve explains why they may now have minutes or seconds, while vulnerabilities can move from disclosure to exploitation within days. Traditional weekly scans and handoffs to patching teams struggle when attackers operate at machine speed. Arve offers a useful framework for understanding security posture through threats, exposures, assets, and controls. Technology changes constantly, but these categories give leaders a way to assess risk without chasing every new term. He also explains why reducing attack surface can begin with basic questions. Does a system need to be accessible from the internet? Does a web server also need remote management exposed? Does a midsize business benefit from spreading its workloads across every major cloud provider? We examine the difficult balance surrounding AI adoption. Blocking every new tool can prevent employees from benefiting from useful technology, but allowing unrestricted adoption creates new exposure. Arve argues for deliberate choices and guidance that reduce risk without stopping progress. The conversation also addresses AI-guided remediation. Barracuda uses AI internally to identify vulnerabilities, but Arve is cautious about fully automated fixes. An AI system may identify a problem and suggest a solution, while a human remains responsible for judging whether the proposed action could damage a production environment. Faster decisions are valuable only when organizations understand the consequences. Arve also considers how entry-level technology roles may change as AI performs more coding and analysis. His view is that people will need to understand how to work with AI, evaluate its output, and carry an idea from design through secure implementation. The role changes, but the demand for human judgment remains. We finish with model sovereignty, data trust, and provider dependency. If a security capability relies on one AI model, leaders need to know whether they can move to an alternative if access, performance, pricing, or policy changes. Arve also explains why Barracuda is preparing to support both open and closed models while the market develops. Where should your organization use AI to accelerate defense, and which security decisions should remain firmly under human control? Listen to the full conversation and share your thoughts with me.
#3736
September 27 · 21 min
Giving Enterprise AI the Context It Needs With Orange Logic
Why do increasingly capable AI models struggle to produce reliable answers inside large organizations? In this episode of Tech Talks Daily, I speak with Misti Vogt, SVP of Engagement at Orange Logic. Her career spans military intelligence, data science, and enterprise content technology, and she also teaches in the DAM and AI program at Rutgers University. That combination gives her an unusually practical perspective on how machines interpret information and why business meaning cannot be assumed. Misti argues that enterprise AI reliability depends on the context surrounding company data. A model may be technically impressive, but it needs to understand relationships, rules, metadata, rights, and intent. Without that layer, it reasons over information originally organized for people rather than machines. The result may sound convincing while remaining disconnected from the way the business defines accuracy, trust, and permitted use. We discuss three forms of context. Static context reflects accumulated knowledge. Transactional context develops through projects and outside information. Semantic context helps systems interpret meaning and relationships across large collections of information. Misti compares this with human conversation. When an answer misses the point, we add information until the other person understands what we mean. Digital asset management sits at the center of this discussion because DAM platforms already organize master data, metadata, transactional data, governance, relationships, and usage rights. Misti believes those structures can give AI applications a stronger business foundation. She also argues that content should become self-aware, carrying information about when it was created, how it was produced, its intended audience, where it has appeared, and how it has performed. Natural language search provides a useful example of why this matters. An employee might ask for creative assets suited to a campaign and welcome a broad set of suggestions. The same employee may then ask for assets licensed for the United Kingdom and United States, with print and web rights for the next 12 months and no use in another campaign during the previous six months. That second request carries business consequences, so the system needs deterministic rules alongside creative choice. Misti also shares an Orange Logic customer example involving a conglomerate with several brands. The company consolidated seven platforms, including three DAM deployments and local storage. Orange Logic then supported shared governance across the group while preserving autonomy for individual brands. Misti says the early results include time savings, improved efficiency, richer metadata collection, and lower costs, although no quantified figures were provided in the recording. The wider question is whether businesses are spending enough time on the information surrounding their content before expanding AI use. Could better metadata, rights management, and business logic produce greater value than another round of model upgrades? Listen to the conversation and share your thoughts with me.
#3735
September 26 · 34 min
Building the Five Foundations of AI Value With Mobile Mentor
What happens when employees begin using AI before their organization has prepared the data, training, controls and measurement required to support them? In this episode of Tech Talks Daily, returning guest Denis O'Shea, CEO of Mobile Mentor, joins me to discuss the 2026 Endpoint Ecosystem Study. The research surveyed 2,500 workers across the United States, United Kingdom, New Zealand and Australia to understand how employees experience their devices, applications, sign-in processes, support systems and workplace AI. The findings show a gap between access and useful adoption. According to the study figures discussed in our conversation, only 29 percent of employees say AI provides regular or indispensable value in their work, while 48 percent report receiving no AI training or do not know whether training exists. Denis says the differences become sharper by sector. Finance has made greater progress with company-wide and role-specific training, while half of the healthcare and government employees surveyed reported receiving no AI training. The generational picture is equally complicated. Denis says Gen Z workers are adopting AI faster than other age groups, but they are also the group most likely to work around company policies when approved tools create friction. If employees cannot complete a task through the sanctioned route, some will use personal accounts and upload company information to public models. The same workers may also need greater support during onboarding, challenging the assumption that digital familiarity automatically means workplace technology fluency. Denis also shares Mobile Mentor's own mistakes. The company deployed Microsoft Copilot to roughly two-thirds of its workforce, ran competitions and encouraged experimentation. When the board asked whether the investment was working, Denis realized he had no dependable answer. The team had not defined use cases, assigned licenses according to the work being done or established a reliable way to measure returns. A subsequent scan found 33,000 sensitive data assets that Denis says were overexposed or shared too widely. Those lessons became what Denis calls the five foundations of AI success. Organizations should define each use case, secure the relevant data, provide training for that use case, build agents around the work and measure the outcome repeatedly. He recommends treating deployments as experiments. If a use case cannot demonstrate a return within three months, the licenses can be reassigned and tested elsewhere. We also discuss passwordless access, the cost of AI tokens and services, and the operational work required to govern growing numbers of agents. Denis believes data, agents and spending will become three immediate management challenges. Each agent will need an identity, appropriate permissions, an owner and a retirement process, while finance and technology leaders will need a clear view of licenses, tokens, API calls and platform consumption. One final lesson reaches beyond AI. Denis says organizations that automated password resets, patching and device provisioning have released technology staff to address newer priorities. Businesses still handling those tasks manually may struggle to find the time needed for data preparation and agent governance. Does your AI strategy begin with another license purchase, or with a defined problem, prepared data and a measurable result? Listen to the episode and share your thoughts with me.
#3734
September 25 · 25 min
Why AI Favors Whoever Automates Most With Barracuda
Can security teams defend an organization when attackers are using AI to research targets, personalize messages, identify weaknesses, and launch campaigns at a scale no human team can match? I returned to Alpbach, Austria, for Barracuda TechSummit 26 and caught up with Neal Bradbury one year after our conversation about being secure today and ready tomorrow. A lot has happened since then. Agentic AI has become a boardroom subject, employee AI use has spread across businesses, and attackers have gained access to tools that lower the cost and expertise required to launch sophisticated campaigns. Neal explains why Barracuda has continued with the unified platform strategy introduced at last year's event. In his view, AI creates additional exposure across identities, applications, email, and data, but it does not make every existing security control obsolete. The immediate requirement is to connect information across these areas and accelerate how quickly security teams can interpret and act upon it. We discuss Barracuda ONE, its Barracuda IQ intelligence engine, the Bailey assistant, Integrated Email Protection, and the recently announced Barracuda AI Data Security offering. Neal also explains why the acquisition of Evo Security adds identity protection at a time when businesses must secure human users, service accounts, and AI agents. One customer example shows why connected telemetry matters. According to Neal, Barracuda's team investigated an attempted wire fraud worth almost a quarter of a million dollars. No single product could see the complete attack. Information from email, network activity, and identity systems had to be combined before the team could understand what was happening. The conversation also examines shadow AI. Employees are already placing workplace information into chatbots and using tools outside approved systems. Neal argues that attempting to ban every tool will send that behavior further out of view. Organizations first need to understand which services are being used, educate employees about the information they can share, and guide them toward approved options. Attackers may have gained the early advantage from AI, but Neal says defenders are catching up through automation. Work that previously took around 45 minutes can now be completed in under a minute inside Barracuda's agentic SOC. The aim is to correlate signals, remove repetitive analyst work, and present fewer alerts with better context. Human judgment remains part of the process when accountability and empathy matter. Do you agree that AI favors the side that automates most, or could excessive automation create another security weakness? Share your thoughts.
#3733
September 24 · 24 min
Inside the Agentic SOC Where Humans and AI Defend at Machine Speed With Barracuda
What does a security operations center need when attacks are arriving at a speed and volume that human analysts cannot manage alone? I recorded this episode with Adam Khan, VP of Global Security Operations and AI Security at Barracuda, during the 20th anniversary of the Barracuda Tech Summit in Alpbach, Austria. Adam has spent over 25 years in technology and security. When we last spoke at the event, he used Home Alone and soccer to make complex security topics easier to understand. This year, expectations were high, and he arrived with Formula One. The comparison begins with what spectators see. Attention naturally falls on the car and driver. Behind them sits a much larger operation involving engineers, strategists, mechanics, simulations, telemetry, and rapid decisions. Adam believes modern security works in a similar way. Customers want to run their businesses, while a largely unseen combination of analysts, threat intelligence, automation, and AI works behind them. That operating model has developed into what Barracuda calls the Agentic SOC. AI agents follow the same playbooks analysts use when examining endpoints, malware connections, artifacts, threat intelligence, identity behavior, and other signals. They can complete repeatable investigative work quickly and consistently across volumes that have grown from hundreds of alerts to thousands or millions. Adam says Barracuda now has hundreds of agents with hundreds of individual skills. These agents can support the process from triage and intelligence gathering through correlation and response. When the system has high confidence that an ordinary user account has been compromised, it may disable that account. If the incident involves an administrative account capable of locking down an entire customer environment, a person must confirm the action. This matters because an AI system can misclassify an event or reach a conclusion that requires additional context. Adam describes feedback mechanisms through which people review decisions, identify mistakes, and feed those findings back into the system. Barracuda also records an audit trail of the actions and queries performed by its agents. One of the most surprising details concerns employment. While headlines frequently associate AI with reducing headcount, Adam says his team has doubled since adopting it. Analysts previously occupied with repetitive investigation have moved into threat hunting, model development, prompt engineering, and attack and defense exercises. The team is also attacking its own systems so that its agents can learn from emerging techniques before a genuine incident occurs. The audience saw this operating model during Adam's keynote. Attendees used their phones to launch controlled business email compromise, QR-code phishing, and ransomware scenarios against Barracuda's attack and defense environment. The platform then analyzed and blocked the activity while the audience watched. Adam says approximately 395 attacks were initiated during the demonstration and all were successfully blocked. That result comes from a controlled Barracuda demonstration rather than an independent test, but it gave attendees a rare view of the speed required during an active incident. We also discuss how Barracuda Managed XDR uses behavior and telemetry across email, endpoints, cloud services, identities, networks, and other technology. An employee traveling with a familiar laptop and phone should not create the same response as an unknown device attempting an unusual login. Historical patterns, device identifiers, signatures, and location data can help reduce unnecessary alerts while highlighting activity that deserves attention. For Adam, the purpose of AI is to increase the speed and reach of security experts rather than remove them. People determine strategy, examine high-consequence decisions, test systems, and remain accountable for customer outcomes. Could the Agentic SOC give security teams the speed they need without surrendering the judgment and accountability customers expect? Listen to the episode and share your thoughts.

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