Welcome back to the Generative Security blog - we took a week off because we were preparing for some exciting conversations with folks around the globe. First, we had the pleasure of presenting at the AWS Sydney Well-Architected User Group about how lessons from the AWS Cloud Adoption Framework and Well-Architected framework can help accelerate generative AI adoption securely. After that, we spoke with the host of the Asia Tech Podcast, Michael Waitze about the direction of generative AI security, why we need to focus on both the technical attacks and the non-technical attacks, and the importance of 3rd-party partners in the near-future.
The first talk was all about the cyclical nature of IT. We've talked about this before in a blog last year, but in this presentation, I used some fresh data to hammer home the striking parallels between the early days of cloud adoption and the current enterprise rush to implement Generative AI. I started by introducing the concept of the technology adoption S-Curve before walking the audience through three core lessons:
My goal was to show that today’s AI security challenges aren't actually some brand-new monster, they're the exact same structural, non-technical, and architectural hurdles we spent the last two decades solving for cloud and container ecosystems. Unfortunately the video ends a bit prematurely, but you can use a copy of the slides to pretty easily fill in the blanks. You can find a copy of the slides here: The cyclical nature of IT - What generative AI security can learn from the early days of cloud
The main reason for this talk is so we can finally stop repeating the incredibly expensive deployment mistakes of our past. My main argument here is simple: over-restricting AI doesn't work. It just drives your team straight into the arms of unmanaged "Shadow AI" on their personal phones. If you check out the video, you'll see how we can transition our security posture from a culture of blockades to an enabling framework of "Yes, And." It’s essentially a macro-level blueprint to help you move at the breakneck speed the market demands without blowing up your data security in the process.
In the live podcast, we tried to cut right through the AI hype to address what it actually takes to manage risk on the agentic frontier. With generative AI, it's important to understand that the attack surface is the conversation itself, so we need to look at the ways a seemingly innocuous conversation could turn malicious. As part of that, we talked about how computers might not have emotions, but they can definitely simulate them - and this can be used to create undesirable outcomes. And this is where the need for a "spidey-sense" comes in. Until LLMs can understand when something "seems off," humans will need to program those guardrails in place. Since we don't have the tools to do this today, there are a couple of things we need to do.
However, threat modeling at this depth is something that most security teams haven’t trained for yet. So bringing in 3rd-parties with tools and expertise to do it for you is critical.
It's important to note that your business will always own the outcomes of any generative AI behavior. So regardless of what Shared Responsibility model you have with your generative AI partners, the outcomes and consequences are almost always exclusively yours. Therefore it's important to understand the risks of what you're building, and focus not only on compliance with things like the EU AI Act or ISO 42001, but also your business risk tolerance parameters as you deploy conversational AI.
Hopefully these two talks helped introduce some new concepts or at least reinforced some of what you've heard in the blog before. As always, if anything piques your interest and you want to learn more, contact us at questions@generativesecurity.ai. Next week we should be back on the same schedule, and there's a lot to go over, so subscribe and make sure you see the next blog!
About the author
Michael Wasielewski is the founder and lead of Generative Security. With 20+ years of experience in networking, security, cloud, and enterprise architecture Michael brings a unique perspective to new technologies. Working on generative AI security for the past 3 years, Michael connects the dots between the organizational, the technical, and the business impacts of generative AI security. Michael looks forward to spending more time golfing, swimming in the ocean, and skydiving... someday.
