DRIFT SHIELD: Behavioral Anomaly Detection for Autonomous AI Systems

TL;DR Autonomous AI systems face a unique threat class: behavioral drift. Malicious prompts, poisoned training data, compromised integrations, and adversarial attacks cause gradual model degradation — the system appears to work but produces corrupted outputs, steals data, or serves attacker interests. Traditional firewalls and IDS systems can't detect drift (the attacks are legitimate API calls from legitimate users). DRIFT SHIELD is a behavioral anomaly detection framework that establishes baseline behavior, detects statistical anomalies, and enforces content sanitization. It's the immune system for autonomous agents. What You Need To Know Behavioral drift is the Silent Breach — Agent appears functional but outputs are corrupted, decisions are poisoned, or data is exfiltrated silently Traditional security can't stop it — Authentication works, TLS works, API signatures validate, but the agent's behavior is wrong DRIFT SHIELD uses three-layer defense: Behavioral Baseline — Profile norma