AI: Trust but Verify

Hosted by Alec Crawford

Technology Business Entrepreneurship NewsInterviews guests

Website RSS feed

Episodes

Latest episode

Jul 2026

Language

About the show

We interview leaders in the AI and finance space to talk about how they are using AI, what to trust, and what to verify. AI risk management and compliance are becoming way more important as AI does more complex tasks. Learn how to do it correctly on the show from experts!

Listen to episodes

60 recent

July 21, 202643 min

AI Governance Begins with Transparency, with Jeremy Snyder

In the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Verapath (https://www.verapath.com), interviews guests about how they are using AI in business, where you can trust AI, and where you need to put up guard rails.In this episode of AI: Trust but Verify, Alec Crawford sits down with Jeremy Snyder, co-founder and CEO of Firetale AI, to explore the rapidly evolving intersection of AI, cybersecurity, and enterprise governance. Jeremy traces his AI journey back to the early days of natural language processing, explains why most organizations already have "shadow AI" whether they realize it or not, and discusses the growing need for visibility into employee and AI agent activity. The conversation covers prompt injection attacks, AI security, open versus closed-source models, China's AI strategy, and why organizations must rethink vulnerability management as AI becomes embedded in critical business processes.Top 5 TakeawaysYou almost certainly have AI in your organization already. Even companies that believe they have banned AI often discover employees are using ChatGPT, Copilot, Gemini, or other public tools with sensitive business information.AI governance starts with visibility. Before organizations can manage AI risk, they need to know which models employees and AI agents are using, what data they're accessing, and whether their activity complies with company policies.The biggest AI security risks extend beyond prompt injection. Vulnerabilities increasingly exist in the surrounding cloud infrastructure, APIs, integrations, and AI marketplaces—not just inside the language models themselves.The next challenge is managing AI agents, not just AI users. As enterprises deploy autonomous AI agents to execute business processes, organizations will need continuous monitoring, telemetry, and governance to ensure those agents stay on task and operate safely.AI is becoming a strategic national security issue. The competition between the U.S. and China is driving rapid innovation, but organizations cannot ignore cybersecurity, patch management, and governance as AI becomes critical infrastructure across financial services and other regulated industries.ResourcesHere are a few of the companies, platforms, and frameworks mentioned during the episode that listeners may find helpful:Firetale AI – AI security and observability platform focused on visibility into employee AI usage and AI agents. https://firetale.aiCyber Risk Institute (CRI) AI Risk Management Framework – AI governance framework developed for financial institutions. https://cyberriskinstitute.orgNIST AI Risk Management Framework (AI RMF) – Widely adopted framework for managing AI risk. https://www.nist.gov/itl/ai-risk-management-frameworkNIST SP 800-53 – Security and privacy controls for information systems used across government and regulated industries. https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/finalISO/IEC 42001 – International standard for AI management systems. https://www.iso.org/standard/81230.html

July 7, 202636 min

Trust, Verify, Repeat: The Future of AI Governance with David Hardoon

In the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Verapath (https://www.verapath.com), interviews guests about how they are using AI in business, where you can trust AI, and where you need to put up guard rails.In this episode of AI: Trust but Verify, Alec Crawford speaks with David Hardoon, former Global Head of AI at Standard Chartered Bank and the first Chief Data Officer of the Monetary Authority of Singapore. With more than two decades of experience spanning AI research, financial services, regulation, and enterprise transformation, David shares practical insights on what it takes to operationalize AI at scale. The conversation explores how AI is reshaping banking, cybersecurity, and regulation, why governance should accelerate rather than hinder innovation, and why organizations must move from static controls to continuous learning and verification as AI capabilities rapidly evolve.TakeawaysGovernance enables innovation. Principle-based, risk-based AI governance creates trust and accelerates adoption rather than slowing it down.Trust AI—but continuously verify it. AI systems should be monitored and validated continuously as models, risks, and business environments change.Banks need a new operating model. The era of "deploy and forget" is over; AI requires continuous adaptation, faster cybersecurity responses, and ongoing process improvement.AI is transforming cybersecurity. Frontier models are dramatically increasing both attackers' capabilities and defenders' tools, making resilience and rapid response more important than ever.The greatest value of AI is augmenting people. Beyond automation, AI empowers individuals and organizations to communicate better, solve harder problems, and unlock entirely new ways of working.ResourcesDavid Hardoon – https://www.davidroyhardoon.com/David Hardoon on LinkedIn – https://www.linkedin.com/in/davidhardoon/The AI Failure Handbook: The Ultimate Guide to Screwing Up Your AI Transformation (David Hardoon) – https://www.amazon.com/s?k=The+AI+Failure+Handbook+David+HardoonStandard Chartered – https://www.sc.com/Monetary Authority of Singapore (MAS) – FEAT Principles for Fairness, Ethics, Accountability and Transparency in AI – https://www.mas.gov.sg/

June 23, 202635 min

AI Guardrails > AI Models for Regulated Industries

June 9, 202641 min

Dominick Romano: Watch Out for Foreign Influence in Our AI

In the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Verapath (www.verapath.com), interviews guests about how they are using AI in business, where you can trust AI, and where you need to put up guard rails. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.AI: Trust but Verify — with Dominick RomanoAbout the GuestDominick “Dom” Romano is the founder and CEO of Drainpipe.io, an AI company focused on making AI systems trustworthy enough for regulated, high-stakes environments. Dom’s background spans video game development, casino gaming, advertising, real-time routing for hazardous payloads, mainframe engineering for IBM z/OS and banking systems, real-time transactions, and observability. Today, Drainpipe.io works with major manufacturers in Germany across pharmaceuticals, automotive, and chemical manufacturing, helping them deploy AI systems where the inputs and outputs must be verifiable—especially when AI touches critical regulatory data such as pharmaceutical dossiers submitted to health authorities.Top 5 TakeawaysAI adoption in regulated industries requires trust, not just capability. Dom emphasizes that when AI touches critical workflows—especially in pharmaceuticals, compliance, or regulatory submissions—organizations need confidence that both the data going in and the AI-generated output are legitimate and trustworthy.AI can scale mistakes, bias, and discrimination. The conversation highlights how AI systems used in decisions such as lending or hiring can create large-scale harm if bias or unlawful discrimination goes undetected—particularly when companies cannot prove the model is not using impermissible factors.Cybersecurity has to come before “cool” AI features. Dom and Alec discuss how rapidly adopted AI tools can create serious security risks when they go viral without proper cybersecurity foundations. The OpenClaw example is used as a warning about software that provides value but fails to account for security from the start.AI is becoming a geopolitical and cultural force. Dom raises concerns about “digital colonialism,” where countries in the Global South may become increasingly shaped by Western AI models that do not represent their own cultures, languages, or values.The near-term AI risks may be more urgent than distant sci-fi scenarios. While existential risk is discussed, Dom argues that immediate threats—deepfakes, multimodal AI on devices, AI in armed conflict, and rapidly expanding cybersecurity vulnerabilities—deserve serious attention right now.People and Organizations MentionedDominick “Dom” Romano — Founder & CEO, Drainpipe.ioWebsite: https://drainpipe.ioLinkedIn: linkedin.com/in/domromanoX: https://x.com/dromanocpm Dom specifically points listeners to LinkedIn, X under the handle @drobmanocpm, andAlec Crawford — Founder & CEO, Verapath; host of the conversation linkedin.com/in/aleccrawfordVerapath — Secure AI platform for financial institutions https://verapath.comFull Sail University — Dom’s educational background in video game developmenthttps://www.fullsail.eduIBM z/OS — Mainframe platform referenced in Dom’s backgroundhttps://www.ibm.com/products/zosEU AI Act — European AI regulation discussed in the episode https://artificialintelligenceact.euJP Morgan / JPMorgan Chase — Mentioned in the discussion of AI, attrition, and jobshttps://www.jpmorganchase.comAnthropic — Mentioned in the discussion of AI existential risk https://www.anthropic.comRay Kurzweil — Mentioned as one of the people concerned about AI risk https://www.kurzweilai.netElon Musk — Mentioned in the discussion of AI risk https://x.com/elonmuskMarcus Hutter — AI superintelligence researcher mentioned by Alec https://www.hutter1.netDoraemon — Referenced as an example of Japan’s more positive cultural association with AIhttps://dora-world.comOpenClaw — Referenced as an example of a viral software/AI-adjacent tool with serious cybersecurity concernsMythos — Referenced in the discussion of emerging cybersecurity threats and online infrastructure risk

June 2, 202638 min

The AI Business Revolution Is Just Beginning, with Tim Sears, Ph.D.

In the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Verapath (www.verapath.com), interviews guests about how they are using AI in business, where you can trust AI, and where you need to put up guard rails. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.Tim Sears is the Chief AI Officer at HTEC, a deeply technical consulting and engineering company with roughly 3,000 people and about 2,500 engineers, working across sectors with clients that include hyperscalers, large tech companies, and a significant share of the Fortune 100 Tim’s career started on Wall Street in the bond markets, where he and Alec were part of the wave bringing technology into finance; they later lived through the dot-com boom at Morgan Stanley before reconnecting in the AI era Tim also worked at Target with data science and engineering teams on early AI-related work, and before HTEC he led software applications at Groq, where the team built AI accelerator technology for high-speed inference .5 Key TakeawaysAI will be bigger than the internet boom. Alec and Tim compare the current AI wave to the dot-com era around 2000, with Alec noting that AI could be the most dramatic technology shift of the next five years .AI strategy is not just turning on tools. Tim argues that giving everyone AI tools is not a real strategy; the bigger opportunity is using AI as a catalyst for teamwork, better processes, and faster execution with humans still playing a critical role .The hard part is organizational redesign. For CEOs, Tim says AI adoption should start with business goals like revenue growth or cost reduction, but leaders need to understand that AI will force a redesign of the organization, skills, workflows, and leadership approach .AI risk management needs “trust but verify.” The conversation emphasizes moving from low-risk internal uses to higher-risk external applications carefully, with more human oversight as risk increases — especially in sensitive areas like healthcare, customer service, and regulated industries .Ethics has to come from leadership, not just engineers. Tim’s view is that “AI ethics” is really just ethics: company values must show up in business decisions, and engineers can help build what leaders want, but they should not be solely responsible for deciding what ought to be built .LinksHTEC — Tim’s current company: https://htecgroup.comGroq — AI inference accelerator company Tim previously worked at: https://groq.comMorgan Stanley — where Alec and Tim worked during the dot-com era: https://www.morganstanley.comGoldman Sachs — where Alec and Tim first met: https://www.goldmansachs.comTarget — where Tim worked with data science / engineering teams: https://www.target.comNVIDIA — discussed in the context of GPUs and AI compute constraints: https://www.nvidia.comMarcus Hutter — AI / AGI / superintelligence researcher mentioned in the episode: https://www.hutter1.netReid Blackman — mentioned in the discussion of AI ethics: https://www.reidblackman.comLarge Language Models / LLMs — central model architecture discussed: https://en.wikipedia.org/wiki/Large_language_modelWorld Models — mentioned as a possible future AI architecture direction: https://en.wikipedia.org/wiki/World_modelQuantum Computing — discussed as another major future technology wave: https://en.wikipedia.org/wiki/Quantum_computingPost-Quantum Cryptography — related to the “Q-Day” discussion about quantum computers breaking encryption: https://csrc.nist.gov/projects/post-quantum-cryptographyVerapath — platform mentioned in the episode: https://www.verapath.com

May 26, 202659 min

The AI Risk No One Sees Coming — with Kriste Krstovski of Columbia University

In the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.Kriste Krstovski is an Associate Research Scientist at Columbia University’s Data Science Institute and an Adjunct Assistant Professor at Columbia Business School, where his work focuses on machine learning, natural language processing, and practical AI systems for social good, business, and healthcare. His research spans predictive modeling, decision-making systems, financial analytics, combating misinformation, and healthcare applications, with a particular emphasis on how AI can be designed, evaluated, and deployed in ways that are useful, reliable, and socially beneficial. (datascience.columbia.edu)In this episode of AI: Trust but Verify, Kriste explains the difference between AI that is merely impressive and AI that is genuinely trustworthy. Impressive AI creates “wow” moments, but trustworthy AI is optimized for reliability in real-world conditions. The conversation frames AI risk as a systems problem, not just a model problem: outcomes depend on the data, deployment context, user interface, objectives, oversight, and safeguards around the system.A major theme is the ethical risk of using AI to make high-stakes judgments about people based on incomplete or proxy data. Kriste warns that AI systems can make wrong inferences about individuals, reinforce bias across populations, and create decisions that people may not understand or be able to challenge. He also discusses misinformation and virality, noting that systems optimized for engagement can amplify what spreads rather than what is true.The episode also explores how AI is changing software development and the future of work. Kriste is especially concerned that students and new employees may become good at generating code with AI but weaker at debugging, testing, and reasoning through failures. The central takeaway is that as AI becomes more capable, human expertise must shift toward verification, evaluation, and governance. Kriste’s final warning is less about one dramatic AI failure and more about gradual erosion: society may normalize manipulation, dependency, and diminished judgment unless governments and institutions become more proactive rather than reactive.Kriste can be reached at kriste.krstovski@columbia.edu, and his Columbia homepage is available here: https://www.columbia.edu/~kk3161/. His book discussed in the episode is Practical AI for Business, described as a practitioner-friendly guide to machine learning and NLP concepts, with plain-language explanations and hands-on examples; it is forthcoming from Columbia University Press.

May 12, 202649 min

Elie Bursztein of Google DeepMind on Mythos and the Cybersecurity Wake-Up Call for Financial Services

In the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec speaks with Elie Bursztein, researcher at Google DeepMind, about why Anthropic’s upcoming Mythos model has become a major wake-up call for cybersecurity and critical infrastructure. Elie explains that AI-driven vulnerability discovery appears to be materially improving, which means the biggest near-term challenge is not just finding flaws but triaging, patching, and operationalizing defenses quickly enough. He outlines what bank and financial-services leaders should be asking their CTOs and CISOs now, including whether their organizations can absorb a wave of patches, prioritize exploitable vulnerabilities, and stress-test their most important systems. The conversation also explores how AI is reshaping penetration testing, bug bounties, SaaS versus in-house software decisions, and the broader systemic risk posed by shared providers and crypto-related systems. Alec and Elie close on a more optimistic note, discussing how increasingly reliable agents can remove drudge work, improve financial education, and raise the baseline of practical expertise for more people.Summary:Mythos Wake-Up Call: Elie argues that new AI models are meaningfully improving vulnerability discovery and raising the urgency of cyber preparedness.Patching Readiness: Organizations need to test whether they can handle sustained bursts of patches across both vendor software and internal code.Smarter Triage: AI-assisted reproduction and exploit testing can help security teams focus first on the vulnerabilities most likely to cause real harm.Systemic Financial Risk: Banks must map dependencies on core providers, segregate critical systems, and plan for degraded or offline operations.AI’s Practical Upside: More reliable agents can automate repetitive work and help broaden access to useful financial and technical guidance.Referenced in this episode:Companies/Organizations:Google DeepMindAnthropicFirefoxFDICU.S. TreasuryVerapathSWIFTOpenAIGoogleFiservJack HenryCOCCAmadeusCapital OneNiceHashCopyright © 2026 by Artificial Intelligence Risk, Inc.

May 5, 202656 min

Cole Wyeth, PhD Student at the University of Waterloo, on Why We Should Wait to Build Superintelligent AI

In the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this deep dive episode, Alec speaks with Cole Wyeth, PhD student at the University of Waterloo focused on AI safety and agent foundations, about why the long-term risk of superintelligent AI deserves far more attention today. Cole explains that aligning advanced systems with human values is extraordinarily difficult because ethics and preferences are hard to specify, and he argues that corrigibility, ambiguity awareness, and deference to humans are essential design goals. He also discusses how ideas like imprecise probability, embedded agency, and multi-agent dynamics can help researchers think more clearly about failure modes, reward hacking, and unexpected cooperation between AI systems. Throughout the conversation, Cole compares controlling superintelligence to cybersecurity, warning that a system smarter than its designers may find weaknesses in any safety scheme that looks secure on paper. The episode closes on a cautious note: until we understand how to reliably control self-improving AI, Cole believes society should slow down and wait years, or even decades, before creating superintelligent systems.Summary:Long-Term AI Risk: Cole Wyeth argues that superintelligent AI could become uncontrollable if developed before robust safety methods are in place.Alignment Challenges: He explains that human ethics and values are too complex to formalize cleanly, making alignment an unusually hard technical problem.Ambiguity and Deference: The discussion highlights the importance of building systems that recognize uncertainty and defer to humans in high-stakes situations.Multi-Agent Failure Modes: Cole explores how AI systems may cooperate or behave strategically in unexpected ways, creating new safety and governance concerns.Pause for Caution: His central takeaway is that society should delay building superintelligence until researchers better understand how to control it safely.Referenced in this episode:Companies/Organizations: University of WaterlooVerapathAnthropicOpenAIDeepMindGoogleARCMETRTroutman Street AudioWaters TechnologyCopyright © 2026 by Artificial Intelligence Risk, Inc.

April 28, 202649 min

Jack Hubbard on AI in Banking, Staying Safe With AI, and Building a Career Through Diverse Roles

In the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec speaks with Jack Hubbard, Chairman of St. Meyer and Hubbard, about his accidental path from aspiring sports broadcaster to longtime banker, consultant, and board member. Jack explains why community banks can no longer afford to delay AI adoption, noting that bankers are already using these tools and need secure, institution-approved options instead of ungoverned workarounds. He shares how AI can transform sales preparation and pre-call planning, while emphasizing that CEOs must learn the technology themselves if they want their organizations to use it effectively. The conversation also focuses on ethical AI use, including the need for clear policies, human oversight, role-specific training, and leadership accountability across the bank. Jack closes with practical career advice for younger bankers, encouraging them to find mentors, gain broad experience, attend banking schools, and commit to lifelong learning.Summary:Accidental Career Journey: Jack Hubbard reflects on the unexpected experiences that led him from college radio into a 53-year career in banking and consulting.AI in Community Banking: He argues that community banks must stop waiting on AI and instead provide safe, practical tools for bankers already experimenting with it.Leadership Responsibility: CEOs and senior leaders need hands-on AI understanding so they can fund, guide, and model adoption from the top.Ethics and Governance: Clear policies, human review, and strong training are essential to reduce data risks, compliance issues, and AI misuse.Banker Development: Jack encourages future bankers to seek mentors, pursue rotations, attend banking schools, and stay committed to reading and continuous learning.Referenced in this episode:Companies/Organizations: St. Meyer and HubbardVerapathNorthern Illinois UniversityUnion Bank of ElginFTRHarris BankBMO HarrisSt. Charles Bank and TrustWintrustDynex CapitalCornerstone AdvisorsPerformance InsightsRelProVertical IQLinkedInBlockPeapack Gladstone BankCapital OneFleetAmerican Bankers AssociationWharton SchoolUniversity of WisconsinLSU School of BankingMassachusetts BankersPerry School of BankingMichigan Bankers AssociationSelling PowerBarlow ResearchChicago CubsBooks: Heart SpokenConversations with ProspectsI Know Jack 53 Years of Banking ExcellenceMovies: Animal HouseCaddyshackCopyright © 2026 by Artificial Intelligence Risk, Inc.

April 21, 202651 min

Matthew Rosenquist on AI, Cyber Risk, and the Future of Defense

In the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this deep dive episode, Alec speaks with Matthew Rosenquist, cybersecurity strategist and CISO, about how AI is rapidly reshaping both cyber defense and cyber offense. Matthew explains how new AI models are dramatically accelerating vulnerability discovery and exploit creation, putting pressure on traditional patching, risk management, and incident response processes. He also shares practical guidance for consumers and businesses on defending against AI-powered phishing, deepfakes, account compromise, and unsafe use of public AI tools. The conversation highlights why strong fundamentals like multi-factor authentication, least-privilege access, segmented data practices, and careful verification matter more than ever in an AI-driven threat landscape. Alec and Matthew close by exploring the emerging risks of agentic AI and MCP-connected systems, emphasizing that companies must adopt AI security controls with urgency, discipline, and realistic expectations.Summary:AI-Driven Vulnerabilities: Matthew discusses how advanced AI models can find and exploit software flaws far faster than traditional security processes can handle.Consumer Cyber Hygiene: The episode stresses multi-factor authentication, account alerts, password discipline, and skepticism toward emails, texts, calls, and social media interactions.Deepfakes and Social Engineering: AI is making scams more personalized, scalable, and convincing, which means users must verify before trusting.Enterprise AI Risk: Companies need to be cautious with sensitive data in public AI tools and apply strong governance to internal AI deployments.Agentic AI Security: Granting broad permissions to AI agents creates major new attack surfaces, making least-privilege design and access controls essential.Referenced in this episode:Companies/Organizations: VerapathAnthropicGoogleWestern UnionSalesforceCopyright © 2026 by Artificial Intelligence Risk, Inc.

Is this your show?

Claim this listing to keep it up to date, reach guests who want to pitch you, and manage bookings with Guestify.

Claim this listing

More Technology podcasts

AI: Trust but Verify

About the show

Listen to episodes

AI Governance Begins with Transparency, with Jeremy Snyder

Trust, Verify, Repeat: The Future of AI Governance with David Hardoon

AI Guardrails > AI Models for Regulated Industries

Dominick Romano: Watch Out for Foreign Influence in Our AI

The AI Business Revolution Is Just Beginning, with Tim Sears, Ph.D.

The AI Risk No One Sees Coming — with Kriste Krstovski of Columbia University

Elie Bursztein of Google DeepMind on Mythos and the Cybersecurity Wake-Up Call for Financial Services

Cole Wyeth, PhD Student at the University of Waterloo, on Why We Should Wait to Build Superintelligent AI

Jack Hubbard on AI in Banking, Staying Safe With AI, and Building a Career Through Diverse Roles

Matthew Rosenquist on AI, Cyber Risk, and the Future of Defense

Is this your show?

More Technology podcasts

One moment, please...

One moment, please...

One moment, please...

Swallow Your Pride Podcast

One moment, please...

One moment, please...