Tech Talks Daily

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.

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.

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.

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.