AEGIS-SELF-HEALING-AI-IMMUNE-SYSTEM
DERIV
Traditional WAFs fail against LLM attacks because hackers use context, not just keywords. AEGIS is an Autonomous AI Immune System designed to protect AI agents. It features a 4-layer defense grid: instant heuristic blocks, vector memory for known threats, and a Sentinel AI that detects novel zero-day attacks. Uniquely, AEGIS is self-healing, when a new attack is spotted, the system instantly "immunizes" its memory, blocking future attempts in milliseconds. Sophisticated attackers are routed to generative honeypots, wasting their time with fake data while the core agent remains secure and compliant.
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"Constructive feedback Clarify limits and validation of self-healing While self-immunization is compelling, it’s unclear how the system handles completely novel attacks or avoids false positives. Providing coverage metrics, examples of attack scenarios, and fallback mechanisms would increase confidence in reliability. Demonstrate scalability in real-world AI deployments The platform relies on multiple defense layers and real-time responses. Showing how it scales with high-throughput or multiple agents without latency or operational overhead would strengthen the case for adoption. Positive feedback Highly original and proactive security approach The combination of self-healing, zero-day detection, and generative honeypots is innovative, addressing a critical emerging problem in AI agent security with a clear, demonstrable methodology."
Mallika Rao
Engineering Leader
For Innovators & Creators