Tech Talks Daily

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.

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.

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.

How should security leaders respond when AI-powered attacks compress detection and response windows from minutes to seconds?
At Barracuda TechSummit 26 in Alpbach, Austria, I spoke with Rohit Ghai, Chief Executive Officer at Barracuda. The conversation took place exactly one year after Rohit joined the company, giving us an opportunity to discuss what brought him to Barracuda, what he inherited, and how his first year has influenced his plans for the business.
Rohit explains that Barracuda's focus on smaller and resource-constrained organizations was an important reason he accepted the role. A cyber incident can threaten the survival of a smaller company, particularly when it has a lean IT team and limited access to specialist security knowledge. For these businesses, Rohit argues that AI-supported and increasingly autonomous security is a practical requirement.
We discuss Barracuda's platform strategy and why genuine integration must extend beyond a shared interface. Rohit compares loosely connected product portfolios to supermarkets. Customers may find it easier to purchase several products from one supplier, but that commercial convenience does not mean the products share data or produce a coordinated response.
Traditional tool sprawl forced analysts to interpret information across several screens. Agent sprawl could introduce systems that act independently, disagree with one another, or take conflicting actions. Rohit believes security platforms must connect information across email, identity, applications, data, and infrastructure so they can reason across the complete attack sequence.
Identity is another major part of the discussion. Barracuda's acquisition of Evo Security addresses privileged access for managed service providers and smaller businesses. Rohit expects machine and non-human identities to greatly outnumber human users, raising questions about excessive privileges and how organizations grant temporary access to autonomous agents.
We also discuss the economics behind AI security. Rohit explains how Barracuda is adapting its value reports to account for token consumption as well as staffing, software, and security outcomes. Barracuda absorbs the direct token costs associated with its own AI capabilities and selects different models according to the task, an approach intended to keep its products affordable for smaller customers and MSPs.
Rohit is cautious about calls for the AI industry to slow development. Coordinating a worldwide slowdown between companies and countries would be extremely difficult. He also argues that cyber defenders cannot pause while attackers continue using widely available models to improve their campaigns.
The conversation ends with a wider leadership question. Rohit believes intelligence will become widely available, making empathy and trust more valuable. AI can generate an answer, but customers, partners, and security professionals must decide whether they trust the organization acting upon it.
Will connected AI security platforms reduce complexity, or could autonomous agents introduce a new form of operational risk?
 Share your thoughts.

What happens when enterprises spend trillions of dollars on AI but the systems underneath it still cannot provide the context those models need to make reliable decisions?
In this episode of Tech Talks Daily, I reconnect with Christian Monberg, CTO at Zeta Global, to examine what separates AI experimentation from production systems that organizations can actually trust.
Our previous conversation focused on how businesses could use AI to scale marketing without losing the human connection with customers. This time, we move deeper into the technology underneath those experiences.
Christian explains why disconnected tools and fragmented data remain barriers to AI adoption, and why Zeta rebuilt its data architecture using Palantir Foundry. We discuss the role of context graphs in connecting customer identity, business objectives, previous decisions, campaign history and outcomes so AI systems can understand more than isolated pieces of information.
We also examine one of the biggest questions surrounding agentic AI: when should businesses allow an AI agent to take action?
Christian shares what enterprises need around explainability, permissions, observability and learning loops before AI systems can safely move from recommendation to execution.
With global AI spending expected to reach $2.59 trillion in 2026, the conversation ultimately comes back to a simple question: how can technology leaders prove that their AI investments are producing measurable business value?