Army veteran. CTO for Infrastructure & Security @ Cisco. Opinions are my own.

Nashville, TN and Akumal, MX
Joined May 2007
We've spent years worrying about quantum breaking cryptography. What if AI gets there first? We've hardwired cryptographic assumptions into decades of infrastructure. Crypto agility matters more than ever.
Replying to @kmad @matthew_pines
I think we might lose public key cryptography.
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TLS 1.3 is a good example of designing for crypto agility. But we need to think beyond protocols and make cryptography replaceable across the entire stack.
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Amy is amazing - we are lucky to have her at Cisco!
Amy Chang leads our AI Threat Intelligence and Security Research team, and her job is to find ways an AI system can be made to do what it's not supposed to. A line leaders should take from her story: “The human can’t just become a rubber stamp… You still have to have your critical thinking cap on; you still have to have ownership.” Great profile from the @Cisco Story Lab team!
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Honored to be speaking here, great event and in my backyard!
Conoce a quienes te inspirarán en Cisco Connect LatAm 2026: nuestro line-up de speakers ya está aquí. #CiscoConnectLatam2026 #Speakers
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Repository Agent Orchestrator is an experiment in running coding agents through a normal GitHub workflow: issue → code → PR → review → human approval → merge. • isolated worktrees per agent • GitHub comments routed back to the coding agent • exact-SHA reviews • convergent multi-agent review swarms • independent verification + adversarial challenge • repo indexing + context routing • persistent structured handoffs between agents • per-role model + reasoning-effort routing • different models for coding, discovery, verification, challenge, escalation, and individual review lanes • bounded compute, convergence and failure policies • durable state + restart recovery GitHub stays the system of record. Humans stay in the loop. It's open source: github.com/cisco-open/reposi…
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Gee, thanks @Apple for keeping me safe.
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I was just explaining this yesterday. “I don’t write code anymore” doesn’t mean I don’t understand the architecture, threat model, Big O complexity of choices made, etc. Some black box functions are definitely more complex than I would have hand coded them - and for sure some are simpler. I don’t need to manually review every line of code to micromanage that anymore and that is one of the biggest speed ups.
The "whiteboard defense:" I should be able to pull you aside at any moment and ask you to explain any customer-facing system you've shipped. You should be able to clearly explain how it works and defend the decisions you made. This is my benchmark for responsible AI usage. I don't expect line-level familiarity with the code. I don't care if you remember the exact function name or implementation detail. You may not even know it. I don't care. But if I ask "why did you do X instead of Y?", "what happens if this actor behaves maliciously?", "what data structure did you use here and why?", or "where does this fail?" you should be able to answer confidently. For PoCs, demos, experiments, whatever: I don't care. Generate 100% of it and understand none of it. Speed over quality every time in those specific scenarios. But if you're shipping customer-facing work, you can't be shipping things you don't understand at a high level.
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This has been a core thesis of work done by @CiscoSecure within products including Hypershield and Live Protect - without embedded active defenses there is no hope to stop AI attackers. It’s not about enabling humans to move faster - it’s about rethinking how we move in the first place.
The uncomfortable truth is that almost everything we built for application security was designed for a different threat model, one where a human had days to think. At this scale and velocity it doesn't hold, and teaching an AI agent to drive those same tools doesn't fix it. Our prior approaches rested on pre-generated heuristics (rules, SAT/SMT) or hypotheses (fuzzing harnesses). They worked because a human could understand the problem first, define the path to a fix, then scale remediation. That luxury is gone. Discovery and remediation now need to happen almost simultaneously, and raising the bar with ineffective mitigations neither stops an AI-driven attacker nor buys time.
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Craig Connors retweeted
Beware people obsessed with outcomes instead of building outcome machines. Its worse than ever with AI, but these people existed before. Short term results above all else, etc. Don't fall into the trap. Invest in building strong fundamentals, invisible supports, and outcomes flow like water. An outcome machine.
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Had the pleasure of meeting with them a year ago and was super impressed with what they were building. Congrats to Chase and team on this milestone!
We're coming out of stealth. We've built our first racks after a successful A0 tapeout, $1B+ in customer contracts, and $800m raised. Early customer tests show us achieving SOTA throughput, latency, and power efficiency on inference workloads. Our first racks ship this summer.
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Every flight without @Starlink is now a disappointment. How am I supposed to push container images on this crap?
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Craig Connors retweeted
The new White House policy requiring green card applicants to apply from outside the US is a capricious attack on legal immigration. It will hurt families, leave us with fewer doctors, teachers and scientists, and hurt American competitiveness in AI.
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Craig Connors retweeted
One of the strangest things about America 🇺🇸… You meet people making $300k/year who are anxious, exhausted, medicated, and can barely sleep at night. Then you go to Mexico 🇲🇽… and see some guy running a small restaurant, barely breaking even… but he’s laughing with friends, drinking mezcal at lunch, hugging customers, and sleeps perfectly fine at night.
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If you aren’t doing this as a leader of software engineers, I think it’s hard for you to grasp the scope of the transformation our industry is undergoing.
I came back to code because AI made it possible for me to build at a level I couldn't before. I'm not coding despite being CEO of YC. I'm coding because this is the most important technological shift since the internet and I'd be an idiot to experience it from the bleachers. I'm 45, running the most important startup institution in the world, and I can ship production software at 2am. That's not a distraction from the job. That is the job understood correctly.
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AI will massively disrupt the legal industry. Administrative staff are often on the front lines of this change. So, she gets experience in AI-assisted law and lets Georgetown learn how to adapt to the future of legal work? Seems like a smart move.
This quoted post is unavailable.
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Craig Connors retweeted
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
The degree to which you are awed by AI is perfectly correlated with how much you use AI to code.
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Honored that Cisco was able to be a part of this. This is not marketing hype nor exaggeration. My take: We have to re-think how we design software, how we patch software, how we test software, how we deploy software. The ideas we pioneered with Cisco Hypershield over the past couple of years were just the start - more to come.
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. anthropic.com/glasswing
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Craig Connors retweeted
Carlini, one of the world best AI security researchers: "I've found more bugs in the last few weeks with Mythos than in the rest of my entire life combined"
Replying to @AnthropicAI
Mythos Preview has already found thousands of high-severity vulnerabilities—including some in every major operating system and web browser.
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Cisco was early in many AI initiatives and we are all learning what “failure” and “learning” look like on a 2 month software cycle riding a 3-5 year hardware cycle. But that doesn’t stop us from experimenting and that’s why I’m here.
I think that if companies are not failing at all with their AI efforts it is a sign that they are not being ambitious enough. This is a fundamentally new technology that we do not know how to use well. Achieving breakthroughs will require experimentation, which require failure.
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