Announcing AI Agents Security Week and calling everyone back to school!
At Yandex School of Data Analysis, we just launched a week-long intensive: “AI Agents Security Week.”
We’ll be learning to save this brave new world of AI agents.
Our recent Saint Highload talk was the culmination of the AI-agent security research we ran while digging into secure agentic development. Audience feedback pushed it to the top of the ranking, a signal the findings land with engineers already wiring agents into their pipelines.
Takeaways and key focus on ADLC security now:
- Observability is the baseline. Also, we observed events on our own honeypot, a fake MCP server left open. Looked at stats, saw attackers probing AI-infra components. Looks like cyber deception gets a second life in agentic world.
- AI red teaming comes next, and it has to cover every component. The whole variety of attack prompts reduces to a starter corpus of queries, then let tooling escalate and adapt them to context. Continuously.
- MCP server is the agent’s hands that define how it changes external systems. That makes monitoring tool calls a priority of its own. And when agent doesn’t know what it’s doing, slap its hands and revoke its access.
Slides and tools are in the repo. Should probably teach the agent to convert slide decks into skills.
The AppSec industry has spent decades in a hard conflict between coverage and precision in threat detection. Classic SAST‑tools generate noise that takes more time to sort through manually than actual threat work. The release of Claude Code Security by Anthropic shook the cybersecurity industry: traditional vendor capitalizations dropped, and the CEO of major player Snyk declared the company’s future must be defined by an AI‑-centric leader.
The market has redefined what makes a security tool valuable. Previously, value was measured in supported rules and languages. Today the formula has changed: what matters is the chain — find, explain, fix. This is where LLMs enter the stage — not as a replacement for classic analyzers, but as an additional interpretation layer. This is how a new category forms: AI SAST.
This article covers how LLMs work with code, why “feeding a repo into a prompt” is a bad idea, which engineering metrics actually matter, and how we research and implement autonomous defect discovery and remediation capabilities for SourceCraft Security products.