AuditChain-AI Brings Cryptographic Oversight and Risk Scoring to Autonomous AI Agents

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Developers built AuditChain-AI, an enterprise AI governance platform, for the HackWithHyderabad 3.0 hackathon to address accountability gaps in autonomous AI agents. The system uses Groq's llama-3.3-70b-versatile model to calculate a real-time Composite Risk Score for every outgoing agent action, flagging prompt injections, secret key exposures, and policy violations before any API call reaches production. Unlike standard guardrails that evaluate prompts in isolation, AuditChain-AI integrates Vectorize Hindsight as a persistent memory layer to track risk patterns, past policy breaches, and admin overrides across sessions. Every decision and policy event is cryptographically signed using Ed25519 keys and chained via SHA-256 hash trees, producing tamper-proof audit logs intended to support SOC-2 Type II and EU AI Act compliance. A Streamlit-based dashboard allows administrators to review high-risk actions and negotiate micro-budget exceptions directly with sub-agents in real time.

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