How Adobe Uses AI Agents for building a WAF Pipeline?
Notes de l’épisode
When vulnerability disclosures shrink the wild exploit window down to less than 24 hours (or even minutes), traditional manual patching and WAF rule creation simply cannot keep up.
In this episode, Ashish sits down with Ammar Alim (Product Security Engineering Lead at Adobe) to break down how to build an automated, agentic WAF pipeline. Ammar shares how his team manages the scale and complexity of seven commercial and open-source WAFs (including AWS WAF, Cloudflare, Akamai, Azure WAF, Wallarm, and ModSecurity) by leveraging AI agents.
Discover how to construct an agentic harness, orchestrate deep research agents to gather exploit POCs, and utilize multi-model architectures (e.g., Anthropic for rule generation and OpenAI as an LLM judge) to eliminate false positives and safely deploy virtual patches
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Questions asked:
(00:00) Introduction & The Currency of Speed in Security(02:00) Ammar Alim’s Background: From Data Centers to Product Security at Adobe(05:00) The Multi-Vendor WAF Nightmare: Managing Scale Across 7 Products(08:30) Understanding False Positives vs. False Negatives in WAF Management(12:30) Why <24-Hour Exploit Windows Demand Virtual Patching(15:30) Pitching Product Leadership: Protecting Release Cycles with WAF Rules(18:30) High-Level Architecture: How the Agentic CVE Pipeline Listens for Disclosures(21:00) Testing Rules in ModSecurity, GitHub Actions, and Shadow Production(26:00) Defining an Agentic Harness: Memory, Constraints, and Context Engineering(32:00) Multi-Model Scoring: Using Anthropic and OpenAI as LLM Judges(34:30) Reinforcement Memory: Training Agents with OWASP and CVE Repetition(37:00) Do You Need to Be an ML Academic to Build Agentic Pipelines?(43:00) Applying Agentic Automation to Other Tedious Security Tasks
Resources spoken about during the episode:
The Tireless Guardian: Agentic AI and the Art of WAF at Scale - Ammar Alim's Talk at fwd:cloudsec