It's been an eventful week. Multiple vulnerabilites in Internet facing software, coding repositories, examples of agent behavior that seem concerning and a flurry of security specialists with solutions that can partially solve your problem. There in lies the challenge. Given the state of IT and security infrastructure, no one solution works. Let's break it down: 1/3 1. Vulnerabilites are being found and will continue to be found as models get more capable, the fragmented nature of the effort denies us the opportunity for a comprehensive find and replace option. 2. Even if all vulnerabilities could be found instantly and patches available - we will be reliant on the ability of enterprises to deploy all these changes at speed. It is not clear what the collateral impact of all these patches will/could be to our infrastructure of deployed en masse. We will need a hybrid patch approach. 3. Virtual patching(VP) is needed in conjunction with direct patching but for a comprehensive (VP) solve you have to be lucky enough to have "modern frontier defenders" (Cyber companies that have vaulted infrastructure that can create patches at speed and push content rapidly to protect their customers) you need them for your network traffic, your traffic to applications on the cloud, your WAF, your end point. 4. There will continue to be patches that will need to be deployed directly but virtual patching will take the heavy load off. 2/3 Medium term solve: Unfortunately token economics and current cyber architectures will not allow for defender swarms to contend with an offensive attack launched using agents. (Some x pundits who aren't steeped in cyber should take heed). Yes, theoretically we should aspire to have these agentic swarms defend us when we are under attack, unfortunately an attack is a sniper moment, chained vulnerabilities, cloaked attack followed by swift exfiltration. It's a speed program not a brute force problem. To solve for this, enterprises need to get their SOC architecture right, logs, feeds, cyber products orchestrated via a modern SOAR for swift response. Only then can we deploy agents to detect and remediate. In addition, enterprises need to modernize and dare I say platformize their perimeter stack to ensure consistency. Unfortunately, enterprises still seem to not see the urgency across the board, decisions are stuck, CIO/CISO's have a lot on their plate. While some honestly have bit seen the scale of such a problem before and it's hard convincing the organization that one needs to open the floodgates on modernizing the IT and security infrastructure. So we will continue to see environments being brought down to patch today it's for PHP, tomorrow it will be for the next one. 3/3 AI deployments are the"wild wild west" - LLM capabilities are expanding faster than we can build products to secure them. Sure an agent will jump across guardrails in a research lab, which is a lesson for all of us to deploy agents securely. But there is a lag between the discovery, the availability of that in a public model and the time needed for the cyber industry to build governance or an antidote. And no, dear frontier lab friends, the world cannot just trust you to build the solve. The infrastructure industry in tech builds and the cyber industry secured and provides assurance to the boards and organizations around security and safety. AI security is the new attack surface. CEOs (present company included) want AI adoption, getting the pace of adoption wrong could be the last decision we make, at the same time "deploying it wrong" could have the same consequence. There are billions of dollars being spent on coding, is your coding harness secure? Start there. If agents can jump guardrails, they can hide stuff in code. Validate business outcomes needed, test your user case and ensure agentic deployments are well guard railed. Deploy a second set of eyes to watch your deployments for drift, compliance and verifiability.

THE PACING IS A NINJA MOVE - But be careful what you campaign for. In any competitive sport, I have seldom found people exercise restraint - they usually have a capture the flag mentality. Don't I want to be the best? The first? The only? - this is how we have been programmed. In the AI race, winning is existential. All AI labs have to race to generate revenue to be able to sustain the enormous amount of committed capital to "not be left behind" in the infrastructure build. There isn't enough room for many. So the desire to slow down is puzzling, but perhaps if the whole system slows down, the rules of winning can be the same for all. Do we have a problem that AI could be a killer? Model capability is a tale of two cities, at one end the models are showing their prowess in tasks like cyber or math as seen recently, so there is likely a probability that the models get extremely powerful and could precipitate a world event. At the same time, in many domains the lack of training data makes the models woefully inadequate. Even in areas like cyber - the LLMs aren't great at the edge cases and generally not economical for the defender case, but great for the attackers. Funnily - in all their "concern" it is still an uphill battle to get them to expose APIs for third party security companies like ours, for us to build robust security for AI adoption. It's slow progress. So why do this? I do believe deep down this is a commercial strategy. A ninja strategy. The liability associated with a model gone rogue has the potential of wiping out the economic opportunity of any frontier company. How do you best show the duty of care? You show that you care. How do you make sure you don't lose out to your competitors? You get them to do the same! If that becomes the industry standard for duty of care, you have a collective first line of defense. Who do you get to govern this? "Yourself" - that is what I think will become the achilles heel. The risk? Open source! China! Countries other than the US! So you ask for a global agreement, because you don't want to be sued in other markets who might even be more punitive. But that was an afterthought - that afterthought will cost. Thks pacing campaign rhetoric has become the talk of the town and it might work. Everyone has jumped into the debate. Both sides of the house, nation states. CEOs (present company included). It's more fun discussing the evil of AI than basics of economic affordability or international trade. The result: We might end up with AI safety boards, regulation in micro jurisdictiona and a fragmented fabric of laws around the world which would make compliance and liability a challenge. Perhaps the intended consequence of pacing would have an unintended consequence of a labyrinth of regulation. Regulation destined to cause a slowdown. Who wins? Simplicity. Open source? Open source is already on its way to gaining more adoption, this could drive it further, faster, each iteration of open source gets closer to frontier LLMs - making it viable to deploy them for more and more use cases. In the end, how will the evaluators know as AI gets smarter, that AI hasn't figured their role out and outsmarts them at their task! That will be the next frontier :)

Security and IP implications of AI The case for rapid AI deployment - I think we can all conclude the following from the last few months of developments - 1. Not using AI could and will become an existential issue for both individual users and enterprises. 2, Enterprise adoption will be cautious while individual users are definitely going to race ahead, try different use cases, build agents, push the models to their limits (although it seems harder to do, unless you live in an AI Lab) 3. Employees and developers will take matters in their own hands since they will find their cautious enterprises aren't moving fast enough. 4. Agents are showing their prowess, uncontrolled, unrestrained agents with a "capture the flag mentality" are showing us the edge cases which demonstrate the negative outcome possiblities of these scenarios. 5. It is impossible to plan for the next 6 months since we can't fathom where technology will evolve to. These activities will cause adoption sans security..... AI has deep implications on security in the future. In this environment security companies need to live on the bleeding edge, anticipating scenarios, building framework solutions so we have a shot at securing future outcomes. Which we all are. Some useful pointers to people planning their AI implementation: 1. Secure what you plan to use, try not to secure the future - no products for security can be created unless we see the future unfold. The future is moving fast, so are we. 2. Most of the coding usage is unsecured. Make sure your coding is secure. Most enterprise AI apps do not offer a secure instance (this is your IP living in their instance)! SECURE your codex, cursor, Claude code Harvey, glean, legora instances now! 3. Do not try and build your own - I have already experienced enterprise customers building gateways and tools for agents - security companies have thousands of specialists working on this, leverage them, Focus on AI adoption instead. Partner to secure. 4. Securing agents is a complex problem - securing the agentic lifecycle - real time inspection and kill switches are key. Don't fall in the discovery and posture trap (Visibility - process understanding - intent interpretation - ability to stop inline are key tenets to the agentic lifecycle - not identity, posture and inventory - those are mere building blocks) 5. Only use enterprise protected models, single tenant, firewalled, inspected implementations - this is your IP you are playing with - LLMs have shown they will cross boundaries to capture the flag - you think your IP is safe? Once you train an unprotected model with your IP - you can't reverse the trade. 6. Perhaps the most important one - do not use a security tool built by the same person who is selling you the AI implementation, historically IT vendors are different from security vendors. You need an enterprise solution for security and it must work on your diverse infrastructure. Use a pure play security partner. Happy building with AI.