Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Last week, Snyk CTO Manoj Nair sat down with Alon Krifcher, Head of Applied AI from Anthropic, for a live discussion on the coming wave of autonomous attacks. Manoj kept landing on one thing: the AI Hurricane has already arrived, and what's left is deciding whether your defense runs at the same speed as the threat.

Attackers don't read your repository; they hit your URL, and chain together whatever they find. In an era where offensive AI runs against live applications at machine speed, your security tooling needs to go beyond finding vulnerabilities to prove exploitability, including whether they can be combined into a breach.

Security teams can spend years tuning DLP rules for the tools they know about. But, then an employee will paste a customer list into an AI assistant that did not exist on any policy six months ago, and the rule never fires. The tool was not on the list, and once the data returns as a summary, it no longer matches the pattern a rule was written for either, creating risk the security team is completely blind to. A bigger rule set does not close this new risk gap.

Most of the industry discourse on agentic AI risk has settled into a comfortable framing: agents get attacked the way models get attacked, through some form of prompt manipulation, and the fix is a better guardrail. I think that framing is dangerously incomplete, and I want to walk through five specific scenarios that make the case directly rather than abstractly. Two of them are manipulation. One exploits trust between agents rather than any single agent's behavior.

Security alerts rarely arrive one at a time. A single alert can cause a spike across the environment, requiring a human analyst to decide which alerts are related and what they mean. When multiple arrive at the same time, it can quickly overwhelm even a seasoned security analyst. Enter the alert paradox. Now, our built-in, multi-AI-agent security operations harness can handle more of this work at Cloudflare scale.

Is your leadership team pushing to roll out new AI tools, but your users are still struggling with basic issues like working on large/complex files or collaborating effectively? Then you’re starting off with an ineffective AI adoption plan. AI is becoming an increasingly important collaboration tool, so it’s critical to include it as part of your foundation. You can even think of AI as being your first collaborator.