The Cyber Business Podcast

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

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

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

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

Guest Introduction: Desmond Grant is the CIO of Littleton Public Schools, a high-performing school district located approximately 15 minutes southwest of the Denver metro area and recognized across Colorado and the nation for its academic outcomes. In his second year as CIO, Desmond oversees technology strategy, cybersecurity, and data governance for a district navigating the same funding pressures, enrollment declines, and AI adoption challenges facing public education nationwide. He brings a practitioner's perspective to every conversation about technology in schools, grounded in a conviction that the decisions made right now about AI literacy will shape a generation.

Here's a Glimpse of What You'll Learn

• Why Desmond says we are no longer in the exploratory phase of AI and what the shift to intentional use actually requires of school districts
• Why his district adopted the principle that it is not about being pro AI or anti AI but about being AI literate and why that reframe changes every conversation
• How Littleton built an AI task force including staff, educators, students, and leaders to produce a framework being released at the start of the 2026 to 2027 school year
• Why data privacy agreements and data governance have to come before any organization can responsibly give AI access to its data
• How Desmond is crowdsourcing cybersecurity expertise internally across every domain on his team without the budget to hire dedicated security staff
• Why machine learning is the unsung hero of the AI security battle and why the MGM breach is the clearest example of what it would have stopped
• Why a prominent AI researcher's claim that AI will be more significant than electricity, including more significant than the Internet, is now getting a much larger show of hands in Desmond's presentations

In This Episode

Desmond opens with a budget reality that shapes everything else he says in this episode. Littleton Public Schools is funded primarily on a per-pupil basis, enrollment is declining across the state and the country, and Colorado's state deficit is creating competition for the same limited dollars between Medicaid, K12, and every other public obligation. In that environment, being a high-performing district does not guarantee resources. It just means the expectations are higher. Desmond's response to that constraint is practical and creative: he is exploring cell tower and colocation partnerships as revenue streams, building a crowdsourced internal security team across every technology domain on his staff, and pursuing a managed security service provider relationship that gives him access to a bench of specialists, including data privacy experts and penetration testers, without the cost of hiring them full time. The framing he uses throughout is the same: when the pot is limited, you get creative about what else is in the room.

The AI governance section of this episode is where Desmond is most candid about what the past year taught him. He felt at the start of his tenure that there was time to develop policy thoughtfully. Twelve months later he looked up and said they were behind. That experience produced one of the most direct and repeatable lines in the episode: we are no longer in the exploratory phase of AI. The district has moved past that point. The response was an AI task force that brought together staff, educators, non-educators, leaders, and students to build a framework organized around four pillars: cybersecurity and data privacy, teaching and learning integration, policy and ethics, and the principle that there must always be a human in the loop. The framework was presented to district leaders and the Board of Education and is being released at the start of the 2026 to 2027 school year. The principle Desmond has adopted as his north star for all of it: it is not whether you are pro AI or anti AI. It is whether you are AI literate. You may feel any way you want about it, but if you are at least informed, you can justify your thinking and your reasoning. That framing has changed how he runs every stakeholder conversation about AI in the district.

The security conversation in this episode is where Desmond and the host find the most alignment and the most productive friction. Desmond makes the machine learning versus LLM distinction in terms that are as clear as any guest this season. Machine learning is not consuming your data and running as an agent. It is asking one question on a continuous loop: is this normal? Adobe encrypting a file it does not normally encrypt. Desmond sending emails at 3:00 AM in a voice that does not sound like him. A new admin account doing things on day one that no other admin does. These are the signals machine learning catches at machine speed, stops, and escalates to a human. That is the model Desmond believes wins the security battle, not blocking everything off, not patching faster, not adding another SIEM or EDR layer, but having a system that sees abnormal behavior across every surface and calls for an adult before the damage is done. The MGM breach, he argues, is the clearest proof of what that would have meant in practice: a new admin account running behaviors no established admin ran, on day one, and no system flagging it as abnormal. Machine learning would have caught it. Nothing else would have.

This episode is brought to you by Cyberlynx

Guest Introduction: Rob Dickson is the CIO of Wichita Public Schools, the largest school district in Kansas, serving just under 50,000 students across 87 schools and programs throughout the Wichita metro area. In a role that spans both operational and instructional technology, Rob oversees cybersecurity and infrastructure alongside a portfolio of forward-looking educational initiatives that includes a public micro school, an immersive coding program, a hub for advanced cybersecurity and machine learning education built in partnership with Wichita State University, and a summer STEM camp serving 800 middle school students. He brings a career that started in the U.S. Air Force and spans 27 years in education technology to one of the most ambitious public school technology programs in the country.

Here's a Glimpse of What You'll Learn

• How Wichita Public Schools built Future Ready Centers where students learn advanced manufacturing, BioMed, and cybersecurity in environments that look nothing like classrooms
• Why Rob draws a sharp line between productive struggle and cognitive offload, and why getting that balance right is the most important AI challenge in education today
• How AI-powered tabletop exercises running on continuous improvement cycles are changing how Rob's team builds and tests its security posture
• Why 900 job applicants for a single data analyst position turned out to be a social engineering threat vector and what Rob did about it
• Why Rob is hiring students from WSU Tech to do real cybersecurity work and refresh 45,000 devices this summer
• Why skills now have life cycles measured in years rather than careers, and what that means for how schools and post-secondary institutions need to rethink what they teach
• Why the superintendent who gives his team room to take risks is the most important ingredient in everything Wichita is building

In This Episode

Rob opens with a description of Wichita Public Schools that reframes what a public school district can look like when leadership decides to build toward industry outcomes rather than test scores. The Future Ready Centers are not classrooms. The advanced manufacturing center teaches students to build planes. The Hack, the new hub for advanced computer knowledge built in partnership with Wichita State University, teaches cybersecurity and machine learning as extensions of computer science, with data science on the way. The micro school called Creative Minds runs on a 2.5-hour instruction model with the rest of the day in project-based learning organized around a year-long theme. This year it was animal conservation. Last year it was food preservation, culminating in a dinner and a show. Rob is explicit that none of this exists without the relationships that came first: with WSU Tech, with Wichita State, with local industry, and with the state Department of Education that had to understand what a school day that does not look like a school day actually is before it could be approved.

The AI and education section of this episode is where Rob makes his most intellectually precise argument. Cognitive offload is real and useful. He does it himself every day to get through the work. But productive struggle cannot be outsourced because the wisdom that comes from working through a hard problem is not transferable. AI can help a student produce an output, but it cannot understand the material from the student's lens, bias, and perspective. That understanding only develops through the struggle, and once it exists, it is what makes a person capable of evaluating AI's outputs rather than simply accepting them. Rob draws the through-line to agentic AI directly: when you build an AI agent, you have to decompose a task to its root level and make it highly verifiable. If the task is not verifiable, subjectiveness enters the picture. And subjectiveness requires wisdom. And wisdom only comes from the productive struggle that most shortcuts are trying to skip. It is one of the more complete and practically grounded arguments for teaching children how to think before teaching them how to use AI that this podcast has featured.

The security section of this episode delivers two concrete and specific examples that most IT leaders outside of education will not have heard before. The first is the 900-applicant problem: Rob posted a data analyst position and received over 900 applications. When his team began vetting them, a significant number were not real people. They were social engineering attempts to get an insider into the district's systems with access to student data. The second is the continuous improvement tabletop model, where instead of scheduling the annual March tabletop exercise and calling it done, Rob's team runs scenarios through AI, posts the results, and uses the memory the system has built to push the next scenario further. The result is a security posture that improves continuously rather than in once-a-year snapshots. Both examples reflect the same underlying principle: the threat environment in a school district is as complex as any enterprise, and the organizations that survive are the ones that treat security as a process rather than an event. This episode is brought to you by Cyberlynx

Guest Introduction Alan Schomaker is the CIO of Loyola University New Orleans, a Jesuit-based institution of approximately 5,000 students that includes a law school and sits on the Gulf Coast as one of four Loyola universities across the country. Five years into his tenure, Alan has led one of the more dramatic technology transformations in higher education, taking the university off a 45-year-old mainframe system and into a cloud-based infrastructure, navigating a hurricane mid-implementation, and now building an AI adoption culture that encourages faculty and staff to solve their own problems rather than wait for IT to do it for them.

Here's a Glimpse of What You'll Learn

• How Alan led Loyola through a mainframe-to-cloud migration while a hurricane shut down operations mid-implementation
• Why ghost students powered by AI agents are committing financial aid fraud at universities across the country and how Alan's team is detecting them
• Why locking down AI to a single approved tool is short-sighted and what Alan is doing instead to prevent shadow AI from taking root on campus
• How a new registrar used ChatGPT to solve in 16 hours a workflow problem that IT had been unable to crack for a year
• How that same registrar then built an AI scheduling tool that reduced a week-long whiteboard process to 10 minutes
• Why AI writes better SQL than most database administrators and what that means for how technical staff should be thinking about their role
• Why teaching students how to use AI is the same obligation universities have always had with every other powerful tool

In This Episode

Alan's first five years at Loyola University New Orleans read like a case study in change management under pressure. He inherited a 45-year-old mainframe that some staff still describe as the greatest system ever built, navigated the cultural resistance of moving business processes back to the departments that own them, and did it all while a hurricane shut the campus down for a month in the middle of the implementation. The technical migration was the easy part. Getting people to accept that having more control over their own systems was a benefit rather than a burden was the harder work, and Alan is candid that it is still ongoing. What that experience built in him is a clear instinct about where the real friction in technology adoption lives, and it is almost never in the technology.

The ghost student problem Alan describes is one of the most specific and underreported AI threat vectors this podcast has covered. AI agents are being deployed to enroll as fake students in online programs, submit falsified identification documents, collect financial aid and Pell Grant money, and disappear. Alan knows it is not unique to Loyola because he has compared notes with CIOs at other universities and found it spreading. The tell that cracked it open at Loyola was an address verification check that started returning properties actively listed for sale on Zillow. That single data point revealed the fraudulent enrollment pattern and prompted a broader vetting process that now correlates IP location, phone verification, SSN identification, and address data before admissions decisions are made. It is a practical, layered response to a threat that most institutions have not yet acknowledged publicly.

The two stories Alan tells about his new registrar are the best argument for democratized AI problem-solving this podcast has captured in a single episode. The first: a grade change workflow that had defeated IT for a year, attempted through the ERP's native tools, abandoned at 80% completion, and then solved by the registrar in 16 total hours using ChatGPT to build a Google Form with scripting, a logging sheet, automated email routing, an approve-deny button for the associate dean, and a two-day reminder trigger. Simple, elegant, and built by the person who understood the process because he lives it. The second: a class scheduling tool that replaced a week of whiteboard and Post-it note work with a 10-minute automated output, complete with a shareable dashboard for the facilities team to assess building impact before scheduling repairs. Alan's response to both was not to shut them down but to help vet them for security and get them into production. His philosophy is explicit: if IT becomes the bottleneck, shadow AI fills the gap. He would rather be the person staff bring ideas to than the one they hide them from.

Guest Introduction

Michael Massey is the CISO at Reminger Co LPA, a defense-focused law firm handling medical malpractice defense, workers compensation defense, and insurance defense across a large portfolio of client matters. With a background that includes time at IBM Watson Health during what he describes as the early days of AI in healthcare analytics, Michael brings a practitioner's perspective to one of the most data-sensitive environments in cybersecurity: a law firm storing thousands of confidential client records, HIPAA-covered medical files, and privileged communications that cannot afford to be compromised.

Here's a Glimpse of What You'll Learn

• Why Michael's team discovered their own AI-powered security tools were working correctly by accidentally locking themselves out
• How Darktrace Identity and Rapid7 are functioning as the frontline defense layer at Reminger and what real-world triggered alerts actually look like in practice
• Why attorneys citing AI-hallucinated case citations before judges is the most concrete example of what happens when verification stops
• How DLP tools surface genuine insider threat activity and why filtering the noise to find the real signal is one of the hardest ongoing challenges in legal IT
• Why Michael's time at IBM Watson Health gives him a firsthand lens on how fast AI can move from promising to catastrophic when governance is absent
• Why the vendor vetting process has become one of the most time-consuming and frustrating parts of AI adoption in a HIPAA-regulated environment
• Why the cat and mouse game between attackers and defenders will never end and what that means for how security teams should be building their programs

In This Episode

Michael opens with a phrase that stopped the host mid-sentence: you cannot outrun a script. It is the clearest and most economical summary of why AI-powered security is no longer optional that this podcast has captured. When attackers are operating at machine speed, any defensive posture that depends on human reaction time is structurally behind. Michael is not making an abstract argument. He is describing his operational reality at a law firm where confidential client records, HIPAA data, and privileged legal communications are stored across a system that receives attempted intrusions on a regular basis. Darktrace and Rapid7 are not aspirational purchases. They are the tools he relies on daily, and he tells the story of how he knows they work because both he and a colleague locked themselves out of their own systems within the same week by doing something outside their normal behavioral pattern. The AI flagged it, acted on it, and left two security administrators calling each other for help. His conclusion is exactly right: that is not a problem, that is proof.

The legal AI section of this episode is where Michael brings a perspective most security guests cannot. Attorneys at firms across the country are now appearing before judges with case citations that do not exist, sourced from AI systems that hallucinated the precedents with complete confidence and no disclaimer. In the legal world, Michael notes, they have their own term for this now. Law clerks are finding the ghost cases. Judges are calling attorneys to account. Disciplinary counsel is getting involved. Fines, suspensions, and in some cases disbarment proceedings are following. Michael draws the through-line to security directly: the same verification failure that burns an attorney in a courtroom burns a security analyst who acts on a false positive without checking. The tool is only as good as the human process built around it. At Reminger, the challenge is particularly acute because attorneys are naturally risk-averse and because many of them do not realize they are already using AI tools, a fact revealed by an internal survey where staff said they did not use AI while actively relying on AI-powered systems every day.

The IBM Watson Health story is the most historically grounded moment in the episode and one of the more sobering case studies this podcast has featured. Michael was there when Watson Health was doing what he now recognizes as early AI: ingesting thousands of hospital records, building treatment outcome models, identifying that Drug A produced better results than Drug B or C for patients matching specific profiles. It worked. Then it moved into cancer research and it moved too fast, and the result was a patient receiving chemotherapy who did not have cancer, a lawsuit, and the end of Watson Health as a going concern. Michael uses this not as a cautionary tale against AI but as a calibration: the pace of adoption has to be matched to the quality of the governance surrounding it. The organizations and governments that cannot move fast enough to build appropriate guardrails are not being slow. They are being outrun by a technology whose consequences they cannot yet fully anticipate. This episode is brought to you by Cyberlynx

Guest Introduction

Kelley Kage is the CIO of Employers Insurance, a 113-year-old workers compensation carrier operating nationwide that recently expanded into excess workers compensation. Overseeing technology, data, product management, and cybersecurity, Kelley has led one of the most deliberate and results-driven enterprise AI rollouts featured on this podcast, achieving 77% adoption and 90% training completion companywide within the first two months of deploying Claude across the entire organization. She brings a change management framework to AI adoption that is already delivering measurable business value, and a conviction that the insurance industry is not the laggard in this transition but an opportunity to lead it.

Here's a Glimpse of What You'll Learn

• Why Kelley frames governance as an on-ramp rather than a speed bump and how that reframe changes every conversation with skeptical stakeholders
• How Employers achieved 77% adoption and 90% training completion companywide within two months and the sequencing that made it possible
• Why the ROI on their Claude rollout is already paying for itself multiple times over and why those ideas came from business leaders, not the tech team
• Why AI adoption is a change management problem rather than a technology problem and what that distinction means for how you plan the rollout
• Why the companies approaching AI purely from a cost savings or headcount reduction mindset are building toward the wrong outcome
• How Kelley thinks about AI as an equalizer for executive coaching and mentorship access across every level of an organization
• Why being at the front of regulatory evolution is a strategic advantage in heavily regulated industries and how Employers is positioning for exactly that

In This Episode

Kelley opens with a framing that separates this episode from most AI adoption conversations immediately: governance is not a speed bump, it is the on-ramp. That single reframe carries significant weight in a 113-year-old insurance company where the instinct to slow down for compliance is deeply ingrained. Kelley's argument is not that governance should be bypassed in the name of speed. It is that governance built correctly and in advance is what allows organizations to move faster once the guardrails are in place. Partnering legal, HR, and cybersecurity into the AI adoption process from the start is not overhead. It is the architecture that makes rapid deployment possible without the expensive course corrections that come from moving first and asking permission later. For a regulated industry operating under ongoing scrutiny from multiple agencies, that distinction matters enormously and she makes it clearly.

The Claude rollout section of this episode is the most detailed and credible enterprise AI deployment case study this podcast has featured. Kelley is specific about the numbers: 77% adoption rate and 90% training completion in the first one to two months, with ROI already paying for itself multiple times over. But what makes the account valuable is not the metrics. It is the sequencing. The rollout started with the executive team and their direct reports, who went through an intensive training program designed not just to teach them how to use the tool but to retrain how they think about their own business processes. The technology deployment followed the mindset shift, not the other way around. The proof that this sequencing worked came at a companywide event at the end of March, where business leaders presented what they had built themselves with Claude and the value they had already generated. It was not the technology team presenting a roadmap. It was the business owners showing what they had made. That distinction is the one that determines whether an AI rollout produces adoption or produces a tool that 85% of the workforce quietly stops using after 90 days.

Kelley closes with an argument about the future of AI in leadership development that is one of the more original takes the podcast has captured this season. Executive coaching has always been costly and difficult to access, which means it has historically been available only to a narrow slice of any organization. AI changes that by giving anyone at any level a tool they can teach who they are, what they are trying to achieve, and what challenges they are working through. Kelley is careful to note that it works best in partnership with human mentors, not as a replacement for them. But the democratization of access to that kind of reflective, feedback-oriented thinking is a genuine shift in what leadership development can look like inside an organization. Paired with her personal board of directors model, which she recommends to everyone she mentors, it represents a framework for continuous growth that does not depend on seniority, budget, or having the right sponsor in the room. This episode is brought to you by Cyberlynx

Guest Introduction

Keith Trawick is the CIO of Stretch Zone, a practitioner-assisted stretching franchise with more than 420 locations across the country and another 75 to 80 expected to open this year. He joined the organization as employee number one when it made the move from boutique wellness service to scalable franchise brand roughly 12 years ago, helping build the technology infrastructure from the ground up in a category that did not exist before Stretch Zone created it. With a career rooted in subscription-based, member-centric businesses, Keith brings a systems-first perspective to the intersection of AI adoption, franchise operations, and the very human challenge of bringing hundreds of independent business owners along on the journey.

Here's a Glimpse of What You'll Learn

• What it means to build technology for a franchise category that did not exist when you started and how that shapes the systems-first philosophy Keith still operates from today
• Why Keith believes the service Stretch Zone delivers is AI-resilient at the front line and where the real AI opportunity lives on the back end
• How machine learning in security tools is the unsung hero of the current threat environment and why traditional patching alone cannot keep pace with machine-speed attacks
• Why Keith is deploying AI voice agents for inbound and outbound calls across the franchise network and the data foundation problem that has to be solved first
• How he is partnering with SoundHound on voice and Blend on middleware to build an agentic system that respects compliance requirements across hundreds of independently owned locations
• Why outcome-based pricing for AI tools makes more sense than hourly labor for a franchise model and what that calculation looks like in practice
• Why the organizations that wrote off AI after a bad ChatGPT hallucination experience are going to have a very hard time competing from here

In This Episode

Keith opens with an origin story that reframes what technology leadership looks like when you are building the category, not just the company. Stretch Zone did not have a Google Business Profile category to select when it launched because no such category existed. Nobody knew what getting stretched meant. Keith joined as employee one with the franchise growth model and has spent 12 years building the systems infrastructure that allows more than 420 independently owned locations to deliver a consistent, brand-defined member experience without micromanaging the owners running those businesses. That tension, between brand consistency and franchise autonomy, runs through every technology decision he makes, and it is the lens through which he evaluates every AI initiative the organization is now pursuing.

The security section of this episode is where Keith gets most animated, and with good reason. He draws the machine learning versus LLM distinction with a water-in-the-boat analogy that lands harder than most technical explanations do. Traditional patching is reactive by design: the boat manufacturer notifies you of a defect, you patch the hull, done. But zero-day vulnerabilities exploited at machine speed do not wait for the notification cycle. What Keith wants is a system that detects water in the boat as it arrives, identifies where it came from, and addresses it before the hole is officially documented. That is what machine learning tools like Darktrace are doing in practice, and Keith makes a direct case that behavioral AI understanding what is normal for each user, each application, each network pattern, and flagging deviation from that is the only defensive posture that makes sense when the attack pace has crossed from human speed to machine speed.

The voice agent initiative is the most concrete and forward-looking section of the episode. Keith is mid-implementation, weeks away from beta testing at targeted locations, and he is candid about exactly how complicated it is to deploy agentic AI responsibly across a franchise network. The technology problem, building an AI that can handle inbound member calls and make outbound follow-ups within the right guardrails, required choosing partners with deep expertise rather than assembling something from YouTube tutorials and automation harnesses. SoundHound handles the voice side. Blend handles the middleware and data layer. But what took the most work was building the data foundation underneath it: a consistent definition of what a member actually is across 420 locations where 100 different owners might give 250 different answers to that question. Keith is clear that the agentic capability is ready. The last mile is compliance, making sure outbound call campaigns are registered, approved, and respectful of each state's quiet period rules across hundreds of independently owned businesses. That is the problem he is solving in real time, and the fact that he is talking about it before the rollout rather than after makes this episode particularly valuable for anyone who is contemplating the same move.

This episode is brought to you by Cyberlynx