Resilient Cyber

Zack Korman on why the AI safety debate has been captured by an extinction narrative, and why the incident everyone is calling a watershed moment looks a lot more like a preventable security failure.In this episode I sit down with Zack Korman, co-founder of Embroidery, where he uses AI to monitor AI agents, and former CTO of the cybersecurity company Pistachio. I first came across Zack on X during the controversy over fake SOC 2 reports, and he has since become one of the sharpest critics of how AI safety is being framed, funded, and investigated.We get into the effective altruism roots of the existential risk movement, why he argues METR's review of the Hugging Face incident was not independent oversight, and what an actual incident response firm would have done differently. Zack makes the case that alignment is one control among many, that there is no "safe" path in AI, only trade-offs, and that the real risk for most organizations is enterprise environments that were never ready for agents in the first place.In this episode:● How building an AI insider threat product pulled a developer and CTO into the cybersecurity community, and into picking fights on X● Effective altruism, longtermism, and how a focus on preventing extinction came to dominate AI safety● Why "just be careful" misses the point when every path involves trading one risk for another● Dario Amodei's Pacing the Frontier, independent auditors, and why Zack calls bringing in METR "bring your friend to work day"● The funding and relationships connecting METR, Redwood Research, Coefficient Giving, and the labs● True believers versus IPO hype, and why genuine belief does not make someone right● Why many AI doomers believed we were all going to die before they ever learned about computers● The Hugging Face investigation, context drop in AI-analyzed transcripts, and treating knowable facts as unknowable● What Unit 42 or Mandiant would have demanded before putting their name on the report● Eight layers of failure, from a single package proxy at egress to missing monitoring, classifiers, and kill switches● Alignment failure versus containment failure, and why alignment belongs inside defense in depth● The real risks: threat actors misusing AI, enterprise agent deployments, and new attack chains● Why security's risk-averse, laggard culture may be its biggest vulnerabilityChapters:0:00 Intro0:54 Zack's Background1:43 What Turned a CTO Into an AI Safety Critic3:31 Effective Altruism and the Extinction Narrative5:53 No Safe Path, Only Trade-offs6:40 METR, Redwood, and Bring Your Friend to Work Day10:23 True Believers, Hype, or Both12:49 How Doomers Find Their Way Into AI14:39 Taking AI Risk Seriously When It Is Ideological16:56 What an IR Firm Would Have Asked21:10 Eight Layers of Failure in the Hugging Face Incident22:49 Alignment Failure Versus Containment Failure24:32 Alignment as One Layer of Defense in Depth26:22 Why Enterprise Environments Are Not Ready for Agents29:10 Security's Laggard Culture29:19 ClosingConnect with Zack Korman:X: https://x.com/ZackKormanEmbroidery: https://embroidery.ioWebsite: https://zkorman.comResilient Cyber: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners and leaders.#aisafety #aisecurity #agenticai #incidentresponse #effectivealtruism #cybersecurity

Jonathan Rende of Checkmarx on why the model writing your code cannot also be the control that validates it, and why rules-based and AI-driven scanning turn out to find almost entirely different bugs.In this episode I sit down with Jonathan Rende of Checkmarx. Jonathan worked with Fortify and SPI Dynamics back in the day, spent most of the last decade leading product teams in developer and DevOps tooling, and came back to security eighteen months ago because, as he puts it, this is the heart of the hurricane. His argument is that AI is a bigger disruption than the internet, SaaS, or mobile were, not because of any single capability, but because it hits roles, process, and productivity all at once.We get into the two waves he has watched play out with CISOs and their CEOs, why the pendulum has swung back toward program and posture questions in the last quarter, what his research team found when they benchmarked deterministic and probabilistic scanning side by side, and why he thinks agentic AppSec raises the profile of the security team rather than automating it away.In this episode:● Why the first half of 2026 became a real inflection point rather than another AI talking point● The two waves: engineering told to run at any cost, then the pendulum swinging back toward posture and program design● Why functional AI-generated code and secure AI-generated code are still two different things● Separation of church and state, and the conflict of interest in letting the model that generates code also validate it● Benchmarking deterministic and AI-based scanning across dozens of open source projects, and why the overlap stayed consistently under 10%● Fidelity, F1 scores, and the absence of real standards or shared benchmarks in AppSec● Why an incentive to reduce risk and an incentive to sell tokens are not the same incentive● Why agents free AppSec professionals for higher-order work, and why this is not a dark factory● Shadow IT becoming shadow AI, and early scans where half surfaced models, agents, and MCP servers security teams did not know existed● Why new threat vectors show up first in fast-moving unregulated companies while regulated ones see more code-level issues● Low-priority vulnerabilities chained into real impact, and why backlogs now matter as much as incoming code● What to change first: metrics defined up front, in-workflow AppSec, and security reviews that went from annual to monthlyChapters:0:00 Intro0:18 Fortify, SPI Dynamics, and a decade in developer tooling1:24 Why he came back to AppSec2:54 Why the first half of 2026 was the inflection point5:27 Two waves, and the pendulum swinging back9:08 Functional AI code versus secure AI code11:58 Layered defense and the condensed lifecycle15:04 What agents free AppSec teams to actually do17:50 Separation of church and state20:15 Fidelity, F1 scores, and not selling tokens23:25 New threat vectors and organizational maturity25:47 Shadow AI and what the inventory scans found27:58 What to change first in your AppSec program30:44 ClosingConnect with Jonathan:LinkedIn: https://www.linkedin.com/in/jonathanrende/Checkmarx: https://checkmarx.comResilient Cyber: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners and leaders.#appsec #aisecurity #devsecops #shadowai #vulnerabilitymanagement #ciso

Rock Lambros, Co-Lead of the 2026 OWASP Top 10 for LLM Applications, on why prompt injection would have fallen out of the top 10 based on incident data alone, and why the community kept it at number one anyway.In this episode I sit down with Rock Lambros, longtime security leader, former CISO, core team member of the OWASP GenAI Security Project, and Co-Lead of the 2026 OWASP Top 10 for LLM Applications, to dig into the new list and the harder questions underneath it. We get into the letter from the project leads that opens the document, why the LLM and Agentic lists are converging, what a year of messy incident data actually told the working group, and why Rock argues that agency is not authorization.In this episode:Why the LLM Top 10 and the Agentic Top 10 are merging in practiceThe opening letter, and why the industry was slow to admit the model was never the thing to secureWho benefits from a model-centric framing of AI securityPrompt injection ranked number one by practitioners and out of the top 10 by the incident dataWhy incidents get reported by outcome rather than by initial vectorMisinformation, the widest gap between the vote and the dataHidden Context Exposure, soft guardrails, and why instructions and data share one context windowContext rot and why context window management matters for agents doing critical workUsing AI to govern AI, and the dual-layer approach in the Agentic Control StandardAgency versus authorization, and why OAuth is what we have rather than the answerHow to get involved in the OWASP GenAI Security ProjectChapters: 0:00 Intro and Rock's background 1:17 What the agentic work changed about the LLM Top 10 3:59 The opening letter and "It Was Never the Model" 8:48 Incident data and the prompt injection ranking 11:23 Misinformation and the limits of the data 14:24 Hidden Context Exposure and soft guardrails 17:18 The context window and context rot 19:57 Using AI to govern AI 22:49 Excessive agency, and agency versus authorization 27:09 How to get involved with OWASPConnect with Rock:LinkedIn: https://www.linkedin.com/in/rocklambrosOWASP GenAI Security Project: https://genai.owasp.orgGet involved: https://genai.owasp.org/contributingOWASP Top 10 for LLM Applications 2026: https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026/Read my piece on the new list, It Was Never the Model: https://www.resilientcyber.io/p/it-was-never-the-modelResilient Cyber: https://www.resilientcyber.io

Ondrej Vlcek, CEO of AISLE and former CEO of Avast, on why AI vulnerability discovery is not as commoditized as the industry thinks, and why remediation is still the real bottleneck.In this episode I sit down with Ondrej Vlcek, Founder and CEO at AISLE. Ondrej spent roughly 30 years in cybersecurity, joining Avast as employee number six or seven doing kernel-mode driver work on Windows 95, eventually becoming CTO and then CEO, taking the company public and selling it to NortonLifeLock in a nearly $9 billion transaction. He co-founded AISLE in the fall of 2024 to close the loop from discovery through triage, remediation, and verification. His team has now disclosed 350 plus CVEs across projects like OpenSSL and curl.We get into why the moat sits in the system and not the model, why the gray market price of vulnerabilities has not collapsed even as models get cheaper, and what it actually takes to ship a patch a maintainer will accept.In this episode:- Going from Avast intern to CEO, and why vulnerability management was the next problem- The jagged frontier, and why bigger models do not always mean better results- Which classes of bugs got cheap to find and which are still genuinely hard- Why vulnerability prices have not collapsed despite all the model progress- Building a model-agnostic system with bespoke benchmarks for model selection- Sovereign AI, on-prem and air-gapped deployment, and why findings are the real crown jewels- Triage, reachability, and why most findings are not actually exploitable- Patch verification, regression risk, and mitigations for embedded systems that cannot be patched- How AISLE earned trust from curl after Daniel Stenberg killed the bug bounty- Whether a CVE count is a vanity metric- Build versus buy as model capability keeps getting cheaper- What breaks first in the CVE and open source maintainer ecosystem- What AppSec leaders should change next quarterChapters0:00 Intro0:24 From Avast employee number six to a $9 billion exit3:26 Why vulnerability management, and why now5:15 The jagged frontier and what bigger models miss10:36 The economics of finding bugs, and why prices have not collapsed12:11 Building a model-agnostic system with real benchmarks14:30 Sovereign AI, air-gapped deployment, and who sees your findings19:42 Triage, reachability, and why remediation is the bottleneck24:48 Patches that break things, and systems you cannot redeploy25:39 curl, Daniel Stenberg, and death by a thousand slops29:35 Is a CVE count a vanity metric?31:34 Build versus buy when capability keeps getting cheaper34:41 What breaks first in the next 18 months39:46 What AppSec leaders should do next quarter41:13 ClosingOndrej Vlcek on LinkedInAISLEAISLE research and blogResilient Cyber SubstackSubscribe for more conversations with security practitioners and leaders.

Lovable CISO Igor Andriushchenko on soft guardrails vs. hard boundaries, securing vibe coding for non-developers, and building a security program at a 10x company.I sit down with Igor Andriushchenko, Head of Security and CISO at Lovable, the AI development platform behind one of the fastest growth stories in the space. Igor joined as the first security hire when the company was around 40 people. A year later he is running a 20+ person team covering product security, GRC, IT, and platform safety for a company with 400 laptops in MDM and no sign of slowing down.We get into what it actually takes to secure AI-native development, both inside a hypergrowth startup and on a platform where most of the people shipping software are not developers and definitely not security practitioners.In this episode:Building a security program for the company you will be in 12 months instead of the one you are in todaySoft guardrails versus hard guardrails, and how to decide which one a problem deservesWhy hard blocks push AI-assisted workflows into the shadowsRooting guardrail decisions in business goals, risks, and threats rather than tool defaultsDemocratized development without democratized security, and what a platform owes the 99%Lovable's auto-fix toggle, per-app threat models, and the goal of an app with no security tab at allWhether models will ever produce secure code by default, and why defense in depth still carries the loadGoverning the reality that every employee vibe coding an app looks a lot like a new vendorGRC engineering as the way to measure control efficiency layer by layer against AI-powered attackersCRA, NIS2, and the EU AI Act landing on citizen developers who never thought of themselves as software manufacturersChapters: 0:00 Intro 0:23 Igor's background from DevOps to CISO 3:54 Scaling security at a 10x company 6:07 Reinventing the team when growth breaks it 08:26 Soft guardrails versus hard blocks 14:05 Tying guardrails to business risk 17:32 Democratized development, undemocratized security 18:52 Shared responsibility on an AI dev platform 21:16 Auto-fix, per-app threat models, and no security tab 25:21 Will models produce secure code by default? 29:56 Every employee vibe coding is a new vendor 30:57 Enterprise controls, publishing gates, and PII scanning 36:39 AI-powered attackers and why good enough changed 40:43 GRC engineering and measuring control efficiency 46:19 CRA, NIS2, and the citizen developer 52:41 Trust centers for builder apps 54:08 Closing thoughts on the vibe coding communityGuest links: Igor on LinkedIn: https://www.linkedin.com/in/igor-andriushchenko Lovable: https://lovable.devResilient Cyber: Newsletter and episode archive: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders.

CISOs have a stack of tools but no system built to run the security program itself. Mike Armistead wants to fix that.In this episode I sit down with Mike Armistead, co-founder and CEO of Pulse Security AI and a longtime security founder behind Fortify and Respond Software. We dig into why the security leader has never had a system of truth the way the CFO has an ERP and the CRO has a CRM, and how an agentic layer on top of the existing tools can finally close that gap. Mike is measured about where AI gets to decide and where the human stays in the seat, and he shares what surprised him most from research with more than 80 senior practitioners and corporate directors.In this episode:- Why two exits later Mike came back to build a third company around the AI wave- The silos that left CISOs with an acronym soup of tools and no way to run the program- What a system of truth for the CISO actually means and how it layers on top of existing structured and unstructured data- Where agents do the heavy lifting on regulatory monitoring, vendor intelligence, and status reporting- Governing the guardrails, not the keystrokes, and why closing the loop still involves people- What corporate directors actually want to hear in the 15 to 20 minutes a CISO gets each quarter- The findings that stood out, including that 55% of boards have never defined the cyber risk they are willing to accept, and only 12.5% of CISOs are very confident the board leaves with a true picture of the risk- Institutionalizing the tribal knowledge every security program runs onChapters:0:00 Intro0:18 Mike's background and two prior exits1:08 Why the AI wave pulled him back2:21 Why the CISO has no system to run the program4:09 Starting at the program level, not the SOC or AppSec5:28 What a system of truth for the CISO means8:19 Speaking the language of the business9:09 Where AI does the heavy lifting on a typical Tuesday11:57 Govern the guardrails, not the keystrokes15:44 Bringing deputies into the conversation16:46 What the research with senior practitioners found20:37 Boards, risk tolerance, and the reporting gap24:57 AI as a double-edged sword for security leaders25:35 Joanna Burkey and institutionalizing tribal knowledge27:31 A year from now for the security leaderGuest links:Mike Armistead on LinkedInPulse Security on AIMore Resilient Cyber:Substack: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners and leaders.

Alex Pinto, who leads Verizon's DBIR team, joins me to break down the new Breach Impact Study and what data breaches actually cost organizations.For years the industry has argued past itself on breach costs. One camp says the market doesn't care, the other says a single breach ends your business. Alex and his team finally got their hands on roughly 70,000 cyber insurance claims through CyberAcuView, and the Breach Impact Study puts real numbers behind the question. In this conversation we dig into what the data shows, where it stops, and how a security leader should actually use it.Alex Pinto runs the Data Breach Investigations Report team at Verizon Business and has been building the report for close to a decade. The Breach Impact Study is the team's first focused spin-off from the DBIR.In this episode:- How the Breach Impact Study came together and why the DBIR team finally got cyber insurance claims data- Why the study measures insurable loss as a floor, not a ceiling, of real economic impact- The case for reporting medians over averages, and why the team refuses to publish the average- Business interruption versus contingent business interruption, and why downtime moves the needle- Whether an $83,000 median breach impact sends executives the wrong message- The SMB paradox, where the smallest companies take the hardest proportional hit- What the claims data does and does not show about AI on offense and defense- Third-party risk, coverage sub-limits, and the single biggest takeaway for security leadersChapters0:00 Intro0:24 Meet Alex Pinto and the DBIR team2:51 Launching the Breach Impact Study3:26 Getting cyber insurance claims data7:32 Why insurable loss is a floor, not a ceiling11:14 Medians over averages, and why the average is meaningless15:13 Business interruption vs contingent business interruption19:49 Does an $83K median send the wrong message?22:44 The SMB paradox and the cybersecurity poverty line26:05 Where AI shows up, offense vs defense34:48 The CVE explosion and marketing hype36:59 Third-party risk and coverage limits41:34 Wrap-upGuest linksAlex Pinto on LinkedIn: https://www.linkedin.com/in/alexcpsec/Alex Pinto on X: https://x.com/alexcpsecVerizon DBIR and Breach Impact Study: https://www.verizon.com/business/resources/reports/dbir/More from Resilient CyberSubstack: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners and leaders.#cyberrisk #databreach #cyberinsurance #ransomware #aisecurity #dbir

What happens to security investing when vulnerability discovery becomes continuous and exploitation windows shrink from weeks to hours? I sit down with Chenxi Wang of Rain Capital to dig into it.Chenxi is the Founder and Managing General Partner at Rain Capital, a venture fund focused on early-stage cybersecurity companies. She's been a Carnegie Mellon professor, a Forrester VP, and a strategy leader at Intel Security and Twistlock, and her portfolio includes companies like Claroty, ProjectDiscovery, Ox Security, runZero, and Straiker. She closes out my July run of conversations with security investors.In this episode:The AI Exploit Age and why vulnerability discovery is becoming continuousGuardian Agents and the case that it takes an AI to govern an AISeparating AI agent identity from traditional machine identityThe signals that predict enterprise adoption for early-stage security startupsThe barbell funding market and the squeeze on Series B and CWhat security leaders should do differently over the next twelve monthsConnect with Chenxi: LinkedIn: https://www.linkedin.com/in/chenxiwang88/ Rain Capital: https://raincap.vc/ Rain Capital Insights: https://raincapital.substack.comSubscribe to Resilient Cyber for more conversations with security practitioners and leaders: https://www.resilientcyber.io

Bugcrowd founder Casey Ellis joins me to dig into what AI is actually doing to bug bounties, vulnerability discovery, and open source security. We get into his "slopdemic" framing, the curl bug bounty saga, VDP readiness, the pentest market correction, and where security research policy heads next.Casey Ellis is the founder of Bugcrowd, co-founder of disclose.io, and a board member of the Security Research Legal Defense Fund. These days he advises and invests through Tall Poppy Group and works at the intersection of security, AI, and policy. His argument is that the vulnpocalypse was already here, and AI has made the cost of both finding and reporting vulnerabilities collapse at the same time.In this episode:Casey's path from building Bugcrowd to advising, investing, and policy workWhy more practitioners need to get involved in policy, and why law is just codeThe slopdemic vs. the vulnpocalypse, and what actually changed in submissionsAI lowering the bar for a broader, less predictable pool of threat actorsDaniel Stenberg, curl, and maintainers below the security poverty lineThe lightning rod vs. rockets distinction between VDPs and bug bountiesThe pentest market correction underway from AI pricing pressureCollapsing OODA loops, hack-back, CFAA reform, SRLDF, and disclose.ioChapters:0:00 Intro and Casey's background 2:56 Why practitioners belong in policy 6:22 The slopdemic vs. the vulnpocalypse 9:40 AI lowering the bar for threat actors 11:47 Open source, curl, and the security poverty line 15:37 VDP vs. bug bounty readiness 19:20 The pentest market correction 24:20 What breaks first in vulnerability management 27:20 Hack-back and non-cooperative defense 28:43 A near-term playbook for security leaders 31:40 CFAA, SRLDF, and disclose.ioConnect with Casey: LinkedIn: https://www.linkedin.com/in/caseyjohnellis Blog: https://cje.io disclose.io: https://disclose.io Bugcrowd: https://www.bugcrowd.comResilient Cyber: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders.

Jon Sakoda of Decibel joins me to break down AI's impact on cybersecurity startups, venture funding, and why endpoint is the Super Bowl of cyber.Jon is the Founding Partner at Decibel, an early-stage firm backing technical founders in security and infrastructure. He started his career founding IMlogic, an IM security company acquired by Symantec, then spent over a decade at NEA working with companies like Cloudflare, MongoDB, and HackerOne before launching Decibel. We got into why he thinks AI is only magical if you have a magic power, why Decibel led a $100M seed into Ent, and where the firm is placing its next bets.In this episode:Why Decibel operates like the Navy SEALs next to the big platform fundsThe founder community model and finding the early believers among CISOsWhat separates the founders who finish now that AI lets everyone startEnt's $100M seed and the self-driving moment for endpoint securityTelling genuinely AI-native companies apart from AI washingAI eating venture capital and why cyber's best years are aheadOpen models, frontier labs, and why the cat is out of the bagThe agentic SOC, Dropzone AI, and driver assistance vs. self-drivingStartup consolidation cycles and being an N of oneHow buyers and job seekers should evaluate early-stage vendorsDecibel's next bets, from novel AI models to resilience and cyber insuranceChapters:0:00 Intro 0:32 Jon's background and founding Decibel 2:25 Big platform funds vs. specialized firms 3:56 Founders helping founders and early believers 6:22 Scaling beyond the early adopters 7:40 Who finishes the marathon in the AI era 10:19 Founders from outside cyber 12:21 Ent's $100M seed and the endpoint bet 14:53 AI-native vs. AI washing 17:04 AI is eating venture capital 18:55 Open models vs. frontier labs 22:41 The agentic SOC and Dropzone AI 26:03 Consolidation and the startup cycle 29:22 How buyers should evaluate young vendors 31:43 Decibel's next bets and cyber resilience 34:11 Game Day at Black HatConnect with Jon: LinkedIn: https://www.linkedin.com/in/jonsakoda/ Decibel: https://www.decibel.vcSubscribe for more conversations with security practitioners and leaders, and find my writing at https://www.resilientcyber.io