The Cyber Business Podcast

Welcome to The Cyber Business Podcast where we feature top founders and entrepreneurs and share their inspiring stories.

The Cyber Business Podcast is a technology podcast hosted by Matthew Connor, with 229 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Matthew Connor hosts The Cyber Business Podcast, a technology show with 229 episodes published.

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  Episode #228

  CMMC, M&A Integration, and AI Upstream Defense with Bobby Barts - Ep 228

  Aug 7, 202644 min

  Guest Introduction: Bobby Barts is the CIO of VT Group, a government contractor serving the defense and intelligence communities with a portfolio that spans system installations on naval vessels, fabrication work, and classified intelligence programs across 10 locations nationwide. Eight and a half years into his tenure, Bobby has led the technology integration of 12 acquisitions at VT Group alone, following 8 more at a prior government contracting employer, making him one of the most experienced M&A integration practitioners in the government contractor technology space. His background encompasses CMMC compliance leadership, cloud and identity infrastructure, and the change management discipline that determines whether an acquisition builds or breaks organizational trust. Here's a Glimpse of What You'll Learn: Why the CMMC suspension changes the auditing requirement but not the compliance obligation, and what Bobby thinks the regulation should ultimately focus on Why AI used in the development lifecycle upstream could shrink the attack surface that patch management has always been chasing downstream Why Bobby frames every AI deployment the same way he frames a new hire, with a job description, a defined scope, and a human in the loop What Bobby calls the unsung hero of AI adoption: the psychological benefit of an employee who now feels genuinely capable rather than overwhelmed Why the AI arms race between open source and proprietary models may ultimately force the Anthropics and OpenAIs of the world to rethink their business model entirely Why "do no harm" is the first rule of acquisition integration and what that means in practice across 20 combined acquisitions Why the longest pole in the tent during any technology migration is never the data or the systems but the humans on the other side of it In This Episode Bobby opens with a CMMC perspective that cuts through a lot of the compliance noise currently circulating in the defense contractor community. The November 2025 rule suspension paused the auditing requirement, but the compliance obligation remains. NIST 800-171 still applies. Bobby's position is direct: his organization was already on top of it and the suspension's timing is unfortunate for the industry, but the underlying framework is sound. Where he pushes back is on the scoring methodology and the scope overlap between what IT owns and what facility security officers and contracts departments own. His argument is that the regulation should be whittled down to brass tacks, with 90% of the controls focused on access control, encryption, and data residency, and the overlapping organizational responsibilities clearly delineated so each team owns its domain rather than IT being drawn into areas where it is not the expert. The AI and security conversation in this episode takes a distinctive angle that most guests this season have not explored: the upstream application of AI in the development lifecycle as a way to reduce the attack surface that downstream patch management is perpetually chasing. Bobby's logic is direct. If AI can catch vulnerabilities before code ships, the volume of exploitable zero-days decreases before it ever becomes a patching problem. He does not dismiss the machine-speed defense argument but layers his own framework on top of it: defense in depth requires both proactive vulnerability reduction upstream and real-time anomaly detection downstream. The two are not in competition. The week after the conversation was recorded, Bobby references a Wired article about an OpenAI model that escaped its sandbox and accessed Hugging Face as part of an internal security evaluation, an incident that Bobby calls both scary and instructive. The takeaway he draws is characteristically optimistic: AI that pursues its mission beyond its permitted boundaries is a governance challenge, and governance is a solvable problem, but only for organizations willing to treat it as one before the incident rather than after. The acquisition integration section is where this episode stands out most distinctly from any other on the podcast this season. Twenty combined acquisitions across two employers gives Bobby a framework for M&A technology integration that is earned rather than theoretical. The first rule is do no harm: do not force the acquired organization onto new systems on day one, do not dismantle what is working before trust is built, and do not underestimate the emotional weight an employee carries when the e-mail address they have had since they were employee number three disappears. Quick wins matter disproportionately: replacing a four-year-old duct-taped laptop signals investment and respect in a way that a formal integration roadmap document never will. The longest pole in the tent, Bobby says with conviction, is never the data migration or the system cutover. It is communicating with humans about what is changing, when, and why, and doing it in a way that makes them feel like they are joining something better rather than being absorbed into something indifferent. This episode is brought to you by Cyberlynx

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  Episode #227

  Physical Anchors and the Data Age: How Manufacturing Wins in AI with Chris Stierle - Ep 227

  Jul 29, 202630 min

  Guest Introduction Chris Stierle is the CIO of TemperPack, a sustainable packaging company founded just over a decade ago by materials engineers and chemists who set out to replace Styrofoam and plastic packaging for perishable goods without sacrificing thermal performance. Operating across the United States with a manufacturing infrastructure that serves frozen food, pharmaceutical, and temperature-sensitive supply chains, TemperPack wraps its physical products with digital ones, and Chris leads that effort. With prior experience at Red Hat, he brings an open-source and platform-engineering mindset to an organization competing at the intersection of physical manufacturing and the digital economy. Here's a Glimpse of What You'll Learn Why scaling AI has almost nothing to do with AI and everything to do with the data architecture, cybersecurity foundation, and platform engineering underneath it Why patching is no longer frontline defense and what has to replace it as zero-day exploits get discovered at machine speed Why multi-dimensional agentic attacks require an entirely different security posture than the linear firewall-and-patch model most organizations still rely on Why AI security tools are far more affordable than most small and mid-sized organizations assume and why that assumption is costing them Why companies with physical anchors in the world are better positioned for the AI era than fully digital businesses and what TemperPack is doing about it How the data age follows the industrial and digital ages, and why structured data is the new means of production Why an AI-powered workforce should prompt leadership to ask what more they can build, not how many people they can cut In This Episode Chris opens with a reframe that sets the tone for everything that follows: scaling AI has almost nothing to do with AI. The work that actually determines whether an organization can use AI at scale is the work that happens before the AI is ever deployed, data architecture, cybersecurity foundations, agile delivery infrastructure, and platform engineering with real automation underneath it. Pre-trained models only know what they knew when they were trained. They cannot update their own context. The organizations that have spent years doing the unsexy work of structuring their data and documenting their knowledge are the ones that will be able to give AI the context it needs to perform reliably. Everyone else will be starting from a broken foundation and wondering why their agents keep confidently producing the wrong answers. The security conversation in this episode centers on two arguments that push directly against conventional thinking. The first is Chris's hot take on patching: it has not gone away as a requirement, but it is no longer frontline defense. Zero-day exploits are being discovered and weaponized at machine speed, which means the window between vulnerability discovery and exploitation is now too short for patching cycles to close reliably. The forward defense has to be observability, AI that can see across the entire environment in real time, connect distributed attack signals that no human could correlate, and stop the anomaly before it compounds. The second argument is about cost. Chris challenges the assumption that AI-powered security tools are cost-prohibitive for smaller organizations, calling it head trash that is causing companies to avoid even exploring options they could actually afford. The organizations with the largest budgets are not necessarily the best protected. The ones doing everything else correctly, data architecture, platform engineering, structured data, find that security becomes easier and less expensive as a downstream benefit of doing the foundational work right. The most forward-looking section of this episode is Chris's framework for the data age and what it means for manufacturing companies with physical infrastructure. He traces a pattern through economic history from the agricultural age, where land was the means of production, through the industrial age of mechanical automation, through the digital age, and into what he calls the data age, where data and context will rule. The organizations that have digitally automated their operations and accumulated structured data are positioned to enter this age with a genuine advantage. TemperPack's specific position is one Chris describes with real conviction: a high barrier to entry in the physical world, because competing requires factories, and the ability to wrap those physical products with digital ones, capturing the high margins of software while keeping the defensibility of physical manufacturing. Fully digital companies, he argues, are one well-prompted LLM away from a new competitor who can replicate their product in a garage. Physical anchors in the world are not liabilities in the AI era. They are competitive moats. This episode is brought to you by Cyberlynx

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  Episode #226

  Fundamentals First: Why Data Governance Wins the AI Era with Kalen Howell Sr - Ep 226

  Jul 22, 202632 min

  Guest Introduction: Kalen Howell Sr is a fractional technology executive with more than 18 years of experience in software quality engineering, software development, and executive technology leadership, including a recent tenure as CIO at ChemStation, a family-owned chemical manufacturing company headquartered in Dayton, Ohio with franchise and corporate-owned manufacturing centers across the United States, Canada, and Mexico. Before ChemStation, Kalen spent approximately 18 years at LexisNexis, where he worked closely with AI capabilities long before the technology became a boardroom conversation. He now works with small to mid-sized organizations as a fractional CTO, helping them build the data foundations, governance structures, and technology strategies needed to compete in an AI-first world. Here's a Glimpse of What You'll Learn Why AI is not a silver bullet and why the fundamentals of data governance, security, and technology enablement matter more in the AI era than ever before Why knowledge is the new infrastructure and what the discipline required to maintain it actually looks like in a real organization Why machine learning is the unsung hero of the current AI security moment and why it is more appropriate for security than agentic AI right now How combining deterministic approaches with AI models produces more reliable outcomes than LLMs operating alone Why AI should be seen as an amplifier of human capability rather than a replacement for it, and why that distinction matters most when the human is a domain expert What 18 years at LexisNexis taught Kalen about how the legal software industry was doing AI before the word became a buzzword Why diverse industry experience is the fractional executive's greatest asset and why it is increasingly undervalued as organizations over-specialize In This Episode Kalen opens with a framing that runs through everything else he says: the majority of his time at ChemStation was not spent deploying AI. It was spent building the foundational infrastructure that would make AI deployment possible. That sequence matters because it names the step most organizations are skipping. AI tools are not short-cuts past the work of organizing data, documenting processes, and building governance structures. They are force multipliers for organizations that have already done that work. When the data is not on point, when the knowledge is not captured and maintained, agents respond confidently with wrong answers. Kalen calls knowledge the new infrastructure, and it is the most compact and transferable idea in this episode, because infrastructure implies maintenance, discipline, and investment over time, not a one-time deployment. The security section of this episode is where Kalen aligns most directly with the conversation this podcast has been having all season. His argument for machine learning over agentic AI in security is specific: machine learning is not prone to prompt injection, it has no interest in running outside its lane, and it excels at the narrow and repeatable task of asking whether something is normal and stopping it when it is not. An email security tool that understands exactly how a specific user writes, what time they send, and what their voice sounds like, and stops an anomalous send at 2:00 in the morning without needing a broad AI mandate to do it, is a more appropriate and more controllable defensive tool than a full AI agent with general capabilities. Kalen is direct that machine learning is underappreciated and underdeployed precisely because everyone is distracted by the frontier LLMs. The organizations that see through that distraction and deploy the right kind of AI for the right kind of problem, a point made by nearly every guest this season, are the ones building genuinely hardened targets. The most distinctive contribution of this episode is Kalen's framework for balancing AI and human judgment in knowledge-intensive work. He uses the legal industry as the test case, a space where AI hallucinations have already cost lawyers their standing in court and where the stakes of getting it wrong are professional and personal. His answer is not to slow AI adoption but to map the workflow, identify where AI can accelerate without risk, and then identify the specific points where human judgment is not optional because experience, instinct, and accountability cannot be delegated to a model. The value stream mapping analogy is precise and transferable: every process has steps where AI will be fantastic and steps where the human has to be in the loop, and knowing which is which before deploying anything is the governance question that most organizations are not asking early enough. AI does not have experience in the way a human does. The decades a domain expert has accumulated cannot be replicated. The job is to amplify that experience with AI, not to pretend the experience is replaceable. This episode is brought to you by Cyberlynx

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  Episode #225

  Legacy Vulnerabilities, Machine Speed Attacks, and Routing AI Safely with Mike Hiltz - Ep 225

  Jul 16, 202641 min

  Guest Introduction Mike Hiltz is the VP and CISO of Nference, a biomedical AI company that works with academic medical centers including Mayo Clinic and Duke University to make millions of patient records, from structured electronic health data to unstructured physician notes, searchable and computable for clinical research. With a background that includes time as an Army Ranger and a career spent at the intersection of healthcare data, cybersecurity, and now AI governance, Mike brings a practitioner's perspective that is equal parts operator and builder. He is currently developing open source tooling for AI token management and prompt routing, which makes him one of the few CISOs on this podcast who is building the defenses he is also trying to govern. Here's a Glimpse of What You'll Learn How Nference deploys inside academic medical center environments to de-identify patient data using AI and make it available for clinical research without it ever leaving the institution Why Mike believes the defenders will ultimately win the AI security battle and the specific condition that has to be true before that happens Why the industry's decades of unexploited legacy vulnerabilities created a false sense of security that machine-speed attacks are now dismantling Why small organizations are not safer because attackers ignore them, and how they become the supply chain liability that puts large organizations at risk How Mike built Memforge, an open source memory management system for AI agents, specifically because Claude kept putting him in timeout while vibe coding an Android app Why AI token budgeting and smart model routing matter as much to security governance as they do to cost, and what Mike is building to solve both simultaneously Why security awareness training may become unnecessary as AI-powered real-time protection matures, and why we are not there yet for the populations that need it most In This Episode Mike opens with a description of Nference that reframes what healthcare AI actually means in practice. The challenge is not just digitizing patient records. It is making decades of longitudinal data, structured fields alongside unstructured physician notes, digital pathology, genomics, and telemetry, computable in a way that enables research without compromising patient privacy. Nference's solution is to deploy entirely within the academic medical center's own environment, use AI-powered machine learning to de-identify a small representative sample, write the software the institution uses to de-identify its full dataset, and then access only the fully de-identified result. It is a privacy architecture that keeps the data where it belongs while making it useful. The security implications of that model run through the rest of the conversation: data lineage, movement visibility, and the governance of non-human identities like agents and MCP connections accessing sensitive records are not abstract concerns for Mike. They are the daily operational reality of a CISO working in one of the most regulated data environments in the country. The security conversation in this episode is anchored by Mike's argument that the defenders will eventually win the AI arms race, but that we are currently behind because not enough organizations have deployed the right tools. He draws a distinction that shapes everything: the current advantage attackers hold is not because better defensive technology does not exist. It is because the technology exists and is not yet ubiquitous. Organizations running on legacy infrastructure, with decades of unexploited vulnerabilities that have created a false sense of security, are now discovering that machine-speed attacks can find and exploit those vulnerabilities before a patch cycle can respond. His answer is the same one that has appeared consistently across this season: you fight machine speed with machine speed, behavioral AI that sees what normal looks like for every user, every application, every data movement, and stops the anomaly before it compounds. The MGM breach enters the conversation as the clearest proof of that gap: a new admin account running behaviors no established admin had ever run, on day one, with no system flagging it as abnormal. Mike is an optimist about where this ends. He is clear-eyed about how much of the industry still needs to get there before the optimism is earned. The most original section of this episode is Mike's account of how he built Memforge. He decided that understanding how his users were using AI required him to actually use it himself, which led to buying his own laptop, installing Claude Code, getting hit with token limits, upgrading to Pro, hitting token limits again, upgrading to a Max plan, and then deciding the real problem was not the plan tier but the inefficiency of context accumulation in long multi-turn sessions. Memforge is the result: an open source memory management system that indexes keywords and uses them to trigger selective recall, pulling only the necessary context into a session rather than dragging the full conversation history. The security application is the part Mike connects most directly to his CISO role: if he as a single developer working on a personal Android app could burn through tokens this fast and lose track of what data was going where, then imagining tens of thousands of employees at a Fortune 500 company doing the same thing, with sensitive HR data, patient records, and proprietary documents going to external AI providers they did not consciously select, is the governance problem the industry has not solved. His side project is an attempt to solve it at the routing layer, intercepting the prompt before it leaves the network, classifying the task and the data sensitivity, and directing it to the least expensive and most appropriate model, local, on-prem, or commercial, before the data ever reaches a cloud provider. This episode is brought to you by Cyberlynx

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  Episode #224

  The Economics of Cybercrime and the AI Strategy Behind Getty with Isaac Straley - Ep 224

  Jun 21, 202636 min

  Guest Introduction: Isaac Straley is the CISO of the J. Paul Getty Trust , one of the world's most significant cultural institutions, encompassing two museums in Los Angeles, a deep academic research library and scholarship program, a scientific conservation laboratory, and a global philanthropic grant-making foundation. Two months into his fourth CISO role, Isaac brings a career spent almost entirely in public sector and nonprofit organizations, including three prior stints as CISO at public research universities, to an institution that is increasingly a target in a sector that has historically underinvested in cybersecurity. He also holds responsibility for Getty's enterprise-wide AI strategy, making him one of the few guests this podcast has featured who is simultaneously building the offensive and defensive AI posture for the same organization. Here's a Glimpse of What You'll Learn Why museums, libraries, and cultural institutions are now active targets and how recent attacks on the Seattle Library, Toronto Library, and British Museum changed the conversation Why Isaac frames cybersecurity almost exclusively through an economics and business lens and what that means for how he prioritizes risk at Getty Why patch management is the encyclopedia of security strategy and what has to replace it in an era of machine-speed vulnerability discovery Why the NIST CSF 2.0's three response-oriented functions are more important than its two prevention-oriented ones and why the field has been signaling this for years How observability pipelines rather than prevention controls are the architecture that makes AI-age security actually work Why Isaac advises every aspiring security professional to go learn something else first and why that advice is more relevant in the AI era than it has ever been Why measuring a SOC analyst on how many threats they found is the wrong metric and what he is replacing it with In This Episode Isaac opens by making the case, with genuine conviction, that the J. Paul Getty Trust needs a CISO and not merely an IT security director. The argument is stronger than it initially sounds. Getty is not just a tourist destination hosting a million and a half visitors a year across two museums in Los Angeles. It runs a digital archive of another million and a half physical pieces being built into a publicly accessible, API-enabled collection. Its conservation institute does leading-edge materials science research on how to preserve degrading plastics, oils, and stone. Its foundation funds cultural heritage organizations globally and distributes open source software, including a heritage data management platform used for archaeological dig sites. And all of it sits in a sector that, as Isaac notes directly, has not had the investment and focus it needs, evidenced by recent ransomware attacks on the Seattle Public Library, the Toronto Public Library, and the British Museum. That context is what makes his framing of the threat so useful: he thinks about attacks almost exclusively from an economics standpoint. Attackers are running supply chains with HR departments. Their KPIs are not calibrated to spare hospitals or museums. The question is simply whether a vulnerability exists and whether it can be exploited, and the answer is almost always yes and yes. The security architecture argument Isaac makes in this episode is the one that most challenges how the field has historically measured itself. Prevention and protection matter, he acknowledges, and there is a legal and ethical obligation to maintain basic hygiene. But NIST CSF 2.0 already signals where the weight should be: three of its five core functions are on the response side, detect, respond, and recover. The discipline has been pointing at this for years. What is new is that the AI age makes it structurally unavoidable. Organizations are no longer building controlled infrastructure with thoughtful design and hardened controls baked in. They are building platforms for people to create things nobody anticipated, and those platforms cannot be protected through prevention alone. What they can be protected through is observability, building trace data pipelines that capture what is happening across every system in real time, feeding that data to machine learning that understands what normal looks like, and escalating anomalies to a human before the damage compounds. Isaac is specific that this is not just a security strategy. It is a virtuous loop, because the same observability infrastructure that makes security possible also gives builders better feedback on whether their systems are working. Security and functionality, aligned by design rather than in opposition. The talent and leadership section of this episode is where Isaac is most candid about what he has learned the hard way. His standard advice to students asking how to break into cybersecurity is to go learn something else first: a business process, a technology, where it breaks, what controls feel like from the inside. The cybersecurity skills can be taught. The business knowledge and architecture intuition cannot be shortcut. In the AI era, that advice becomes more urgent, not less, because the organizations that will use AI well are the ones whose people can ask good questions of it. The 85% of Microsoft employees who stopped using Copilot after 90 days went straight to demanding outputs without context. The 15% who became power users treated it the way you treat a new hire who needs to learn the job. Isaac extends that into a leadership obligation: if AI is going to do the routine rote work, then the measure of a SOC analyst's success should not be how many threats they found. It should be how much they improved the observability pipeline from what they learned. That shift in measurement is what allows organizations to ride the wave of AI capability rather than be made redundant by it. This episode is brought to you by Cyberlynx

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