Resilient Cyber

Hosted by Chris Hughes

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223

Latest episode

Aug 2026

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EN-US

About the show

Resilient Cyber brings listeners discussions from a variety of Cybersecurity and Information Technology (IT) Subject Matter Experts (SME) across the Public and Private domains from a variety of industries. As we watch the increased digitalization of our society, striving for a secure and resilient ecosystem is paramount.

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60 recent

August 24, 202656 min

Secure Vibe Coding and the 99%

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 today Soft guardrails versus hard guardrails, and how to decide which one a problem deserves Why hard blocks push AI-assisted workflows into the shadows Rooting guardrail decisions in business goals, risks, and threats rather than tool defaults Democratized 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 all Whether models will ever produce secure code by default, and why defense in depth still carries the load Governing the reality that every employee vibe coding an app looks a lot like a new vendor GRC engineering as the way to measure control efficiency layer by layer against AI-powered attackers CRA, NIS2, and the EU AI Act landing on citizen developers who never thought of themselves as software manufacturers Chapters: 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 community Guest links: Igor on LinkedIn: https://www.linkedin.com/in/igor-andriushchenko Lovable: https://lovable.dev Resilient Cyber: Newsletter and episode archive: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders.

August 11, 202631 min

Building a System of Truth for the CISO

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 on Chapters: 0:00 Intro 0:18 Mike's background and two prior exits 1:08 Why the AI wave pulled him back 2:21 Why the CISO has no system to run the program 4:09 Starting at the program level, not the SOC or AppSec 5:28 What a system of truth for the CISO means 8:19 Speaking the language of the business 9:09 Where AI does the heavy lifting on a typical Tuesday 11:57 Govern the guardrails, not the keystrokes 15:44 Bringing deputies into the conversation 16:46 What the research with senior practitioners found 20:37 Boards, risk tolerance, and the reporting gap 24:57 AI as a double-edged sword for security leaders 25:35 Joanna Burkey and institutionalizing tribal knowledge 27:31 A year from now for the security leader Guest links: Mike Armistead on LinkedIn Pulse Security on AI More Resilient Cyber: Substack: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders.

August 5, 202641 min

The Real Price Tag On Cyber Breaches

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 leaders Chapters 0:00 Intro 0:24 Meet Alex Pinto and the DBIR team 2:51 Launching the Breach Impact Study 3:26 Getting cyber insurance claims data 7:32 Why insurable loss is a floor, not a ceiling 11:14 Medians over averages, and why the average is meaningless 15:13 Business interruption vs contingent business interruption 19:49 Does an $83K median send the wrong message? 22:44 The SMB paradox and the cybersecurity poverty line 26:05 Where AI shows up, offense vs defense 34:48 The CVE explosion and marketing hype 36:59 Third-party risk and coverage limits 41:34 Wrap-up Guest links Alex Pinto on LinkedIn: https://www.linkedin.com/in/alexcpsec/ Alex Pinto on X: https://x.com/alexcpsec Verizon DBIR and Breach Impact Study: https://www.verizon.com/business/resources/reports/dbir/ More from Resilient Cyber Substack: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders. #cyberrisk #databreach #cyberinsurance #ransomware #aisecurity #dbir

August 1, 202632 min

Cyber Investing in the AI Exploit Era

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 continuous Guardian Agents and the case that it takes an AI to govern an AI Separating AI agent identity from traditional machine identity The signals that predict enterprise adoption for early-stage security startups The barbell funding market and the squeeze on Series B and C What security leaders should do differently over the next twelve months Connect with Chenxi: LinkedIn: https://www.linkedin.com/in/chenxiwang88/ Rain Capital: https://raincap.vc/ Rain Capital Insights: https://raincapital.substack.com Subscribe to Resilient Cyber for more conversations with security practitioners and leaders: https://www.resilientcyber.io

July 30, 202633 min

AI, Bug Bounties & the Vulnerability "Slopdemic"

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 work Why more practitioners need to get involved in policy, and why law is just code The slopdemic vs. the vulnpocalypse, and what actually changed in submissions AI lowering the bar for a broader, less predictable pool of threat actors Daniel Stenberg, curl, and maintainers below the security poverty line The lightning rod vs. rockets distinction between VDPs and bug bounties The pentest market correction underway from AI pricing pressure Collapsing OODA loops, hack-back, CFAA reform, SRLDF, and disclose.io Chapters: 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.io Connect with Casey: LinkedIn: https://www.linkedin.com/in/caseyjohnellis Blog: https://cje.io disclose.io: https://disclose.io Bugcrowd: https://www.bugcrowd.com Resilient Cyber: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders.

July 29, 202634 min

AI's Cyber Boom

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 funds The founder community model and finding the early believers among CISOs What separates the founders who finish now that AI lets everyone start Ent's $100M seed and the self-driving moment for endpoint security Telling genuinely AI-native companies apart from AI washing AI eating venture capital and why cyber's best years are ahead Open models, frontier labs, and why the cat is out of the bag The agentic SOC, Dropzone AI, and driver assistance vs. self-driving Startup consolidation cycles and being an N of one How buyers and job seekers should evaluate early-stage vendors Decibel's next bets, from novel AI models to resilience and cyber insurance Chapters: 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 Hat Connect with Jon: LinkedIn: https://www.linkedin.com/in/jonsakoda/ Decibel: https://www.decibel.vc Subscribe for more conversations with security practitioners and leaders, and find my writing at https://www.resilientcyber.io

July 23, 202639 min

Why AI Security Is Getting Rebuilt From Scratch

In this episode I sit down with Ed Sim, founder and managing partner of Boldstart Ventures, to dig into where AI security, agentic infrastructure, and the venture market are actually heading. Ed has been an inception-stage investor for nearly 30 years and has run Boldstart since 2010, backing hardcore technology companies across AI infrastructure, cybersecurity, and physical AI. He was the first investor in Protect AI, which sold to Palo Alto Networks in a reported ~$700M exit roughly a year before ChatGPT launched. He is also early in companies like Keycard, Surf AI, and June. About a third of Boldstart's investments are in cyber, so Ed sees this market from the founder and investor side in a way most security conversations do not. We get into why the era of building raw intelligence is giving way to an era of controlling it, what that means for on-prem models and private evals, and why Ed thinks nearly everything in security is going to get rebuilt from scratch. In this episode: - Why a day-one partnership looks different now that anyone can vibe code an MVP - The Protect AI acquisition and what the first exit in AI security signaled to the market - Competing as an inception fund against mega-funds writing giant seed rounds - What founders should actually look for in a venture partner beyond the check - The shift from building intelligence to controlling it, including routing, post-training, and on-prem deployment - Why enterprise data, workflows, and private evals are becoming the crown jewels - Vulnerability chaining, attack path reasoning, and how tools like Mythos are reshaping the security budget conversation - Agentic identity and why Keycard treats agents as short-lived problem solvers rather than digital twins - The Surf AI thesis on automated security hygiene and tying every asset back to an owner - The real bottleneck slowing agent adoption in the enterprise Chapters: 0:00 Intro 0:35 Ed's background and inception investing 1:57 Day-one partnerships in the vibe-coding era 3:53 The Protect AI exit to Palo Alto 6:14 Competing as an inception fund against mega-funds 9:17 What founders should look for in a VC partner 11:48 From building intelligence to controlling it 15:52 Boldstart's domain-specific model portfolio 16:16 Private evals, context, and memory as crown jewels 17:18 Mythos, vulnerability chaining, and attack path reasoning 20:59 How much access should you give the model 22:07 On-prem context and the autonomous workforce 24:49 Agentic identity and Keycard 28:11 Building brand and community with Insecure Agents 31:30 The Surf AI thesis and automated security hygiene 34:13 The real bottleneck to agent adoption 37:09 The easy button, Palantir, and a multi-model world 38:24 Two types of people in this new era Connect with Ed : LinkedIn: https://www.linkedin.com/in/edsim/ Boldstart Ventures: https://boldstart.vc Ed's newsletter, What's Hot in Enterprise IT/VC: https://www.whatshotit.vc More from Resilient Cyber: Substack: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders. #aisecurity #agenticai #cybersecurity #venturecapital #appsec

July 20, 202637 min

Resilient Cyber w/ Joshua Saxe - Why Restricting AI Makes Us Less Secure

Does restricting frontier AI in the name of safety actually make us less secure? Joshua Saxe joins me to make the case that it does, and that AI cybersecurity will be won through defender adoption, not restriction. Josh has spent 15 years at the intersection of AI and security. He built and ran the machine learning program at Sophos, then led security for Llama at Meta, covering security post training, evals, agent guardrails, and prompt injection prevention. He recently left to co-found a startup reimagining vulnerability and exposure management agentically. He also writes one of the most cited blogs on AI and cyber policy. In this episode: - Why restricting frontier model access harms defenders more than attackers - How monitored closed models put threat actors at a structural disadvantage - The jagged frontier, and why attackers don't need frontier models for most of their tradecraft - The national security and supply chain risks of pushing the world onto Chinese open weights models - Why exploits don't cause cyberattacks, and which attacker constituencies AI actually unblocks - The dual use ceiling on guardrails and classifiers - Where defenders should be adopting AI right now, from access management to SOC automation - Using agents to burn down the mountain of security technical debt Chapters: 0:00 Intro 0:42 Josh's background, from blackhat teen to Llama security lead 3:07 The case for diffusion over restriction 6:14 Why restriction hurts defenders more than attackers 10:19 The jagged frontier and what attackers actually use models for 12:49 National security and the supply chain risk of Chinese open weights 16:08 Exploits don't cause cyberattacks 20:20 Where defenders should adopt AI right now 24:20 Guardrails, classifiers, and the dual use problem 27:34 Reimagining vulnerability management with agents 32:17 The structural advantage defenders hold 35:15 Policy wishes and the attacker's Claude Code moment Follow Josh: LinkedIn: https://www.linkedin.com/in/joshua-saxe-01845a1 Substack: https://joshuasaxe181906.substack.com Follow Resilient Cyber: Substack: https://www.resilientcyber.io Subscribe for more conversations with security practitioners and leaders. #aisecurity #cybersecurity #vulnerabilitymanagement #aipolicy #opensourceai

July 16, 202641 min

Cyber Valuations, Moats & the Road to Black Hat

Cybersecurity investor Sid Trivedi of Foundation Capital joins me to dig into AI SOC valuations, services-as-software, moats, and what founders should know heading into Black Hat. Sid is a Partner at Foundation Capital, where he invests at the seed and Series A stage with a focus on cybersecurity and IT infrastructure. This is our annual pre-Black Hat check-in, and a lot has moved since last year, from massive M&A to record-setting rounds in categories like the AI SOC. In this episode: - What has actually changed a year into the AI wave, and what hasn't - Services-as-software, the $4.6 trillion market thesis, and automating cyber workflows across the SOC, IR, pen testing, and threat intel - What AI means for cybersecurity jobs and how practitioners should adapt - Consolidation vs. best-of-breed after Palo Alto's $25B CyberArk deal and Alphabet's $32B Wiz acquisition - AI SOC valuations, including Seven AI's record Series A and Torq crossing a $1B valuation - The double-edged sword of big raises and why founders should be cautious about the valuations they accept - Why you can't simply spend your way to growth in cybersecurity - Moats and defensibility when frontier labs can push into your category - The Black Hat Innovator Investor Summit and the Startup Spotlight competition Chapters: 0:00 Intro 0:52 What's changed a year into the AI wave 2:42 Services-as-software and the AI SOC 9:38 AI adoption and forward deployed engineers 10:57 M&A, platformization, and best-of-breed 14:17 IT and security convergence, plus AI SOC valuations 18:48 Seed-stage risk calculus vs. later-stage investors 21:43 The double-edged sword of big raises 26:20 Why you can't spend your way to growth 29:05 Moats and defensibility in the frontier-lab era 32:25 Deal flow, pricing, and staying disciplined 37:06 Black Hat Innovator Investor Summit 40:17 Startup Spotlight competition 43:28 Wrap-up Black Hat is offering listeners $500 off registration with code USA500Resilient. Connect with Sid: LinkedIn: https://www.linkedin.com/in/siddhanttrivedi/ Foundation Capital: https://foundationcapital.com Resilient Cyber: https://www.resilientcyber.io Subscribe for more conversations with security practitioners, founders, and leaders.

July 11, 202631 min

Building an AI AppSec Engineer

JJ of Gecko Security and former Disney and Costco CISO Ryan Knisley on why AppSec needs an AI security engineer, not another scanner. Description AppSec has been stuck for years, drowning teams in noisy findings that never told them what was actually exploitable. JJ, co-founder and CEO of Gecko Security, and Ryan Knisley, former CISO at Disney and Costco, join Resilient Cyber to talk about what changes when an AI security engineer reasons across code, infrastructure, and design docs at once. We get into why business logic breaks traditional SAST, why attackers think in graphs while defenders think in lists, why MTTR is a broken metric, how Cal.com went closed source in the AI era, and where AI-driven AppSec consolidation lands over the next two years. Key takeaways Gecko is an AI security engineer, not another scanner. It reasons across code, infrastructure, and documentation, so a finding arrives already mapped to whether it is reachable in production and what data it touches. The context that tells you if a bug matters lives outside the code. Business logic, architecture, and runtime are where exploitability is decided, which is why scanning the code alone floods teams with noise. Business logic is why traditional SAST fails, and why an LLM alone will not fix it. The same endpoint with no auth check is a critical bug in a document store and expected behavior in a social app, and only design docs and architecture tell the two apart. Attackers think in graphs while defenders think in lists. A critical with a compensating control may not matter, while ten lows chained together can be the thing that actually reaches the asset you care about. Exploit development is being commoditized. JJ describes a near future where the whole internet becomes one big bug bounty scope with agents running campaign-level attacks, so the old severity-ranking lens no longer holds. Fix the class, not the ticket. Rather than patching bugs one by one, Gecko traces groups of findings back to the design decision that created them and eliminates every variant so the same issue never returns. MTTR is a broken metric. A variant of last week's bug returns with a fresh clock, so teams close tickets to look healthy while risk stays flat, which is why Gecko measures recurrence rate instead. Cal.com shows where open source is heading. After AI coding pushed its pull requests from about 30 a day to 100 with a one-person security team, being open source flipped from an advantage to a liability, so it went closed source and replaced four tools with one. Tool consolidation is a risk decision, not a cost exercise. Ryan's shiny object problem leaves teams stacking scanners nobody can fully staff, and collapsing the stack lets you cross-train people and reduce real complexity. The finding layer collapses, and human judgment moves up. When finding and fixing get cheap, the scarce work becomes deciding what is correct, whether to accept a risk on purpose, and owning the design decision for a whole class of bugs. Chapters 00:00 Meet JJ and Ryan 02:46 Why Gecko is an AI security engineer, not another scanner 05:07 The trend of agentic and headless security tools 05:53 Why business logic breaks traditional SAST 06:27 The no-auth endpoint example and context outside the code 09:06 Attackers think in graphs, defenders think in lists 11:02 Commoditized exploit dev and the internet as one bug bounty 13:55 Shift left and why MTTR is a broken metric 15:07 Eliminating entire classes of vulnerabilities 15:51 Recurrence rate and avoiding risky refactors 18:22 The Cal.com case study and open source going closed 20:48 Consolidation and the shiny object problem in security 22:40 Where AI-driven AppSec lands in two years 27:12 What it takes to trust an AI security engineer 28:57 Where to find Gecko and the Black Hat talk