AI Agent Governance for Data Operations

⚠

The Risk

AI agents with database access can execute queries, modify records, and perform administrative operations. A single bad decision could result in catastrophic data loss:

✗ Without HaltState

Agent decides to "clean up old data" and executes DROP TABLE on production database. 2 years of customer data gone. Backup restore takes 8 hours. $200K in lost revenue and reputation damage.

✓ With HaltState

Agent attempts DROP TABLE. HaltState blocks immediately - destructive operations on production are denied by policy. Agent receives denial. Proof Pack logs the attempted action for security review.

📝

The Policy

Define data protection rules that are enforced at runtime, before the damage happens.

"Deny any database.destructive action on environment = production"

Or with granular controls:

{ "action": "database.*", "effect": "deny", "condition": "params.operation in ['DROP', 'TRUNCATE', 'DELETE'] AND params.environment == 'production'", "reason": "Destructive operations blocked on production databases" }

🔄

The Flow

What happens when an agent attempts a destructive database operation:

Agent attempts
DROP TABLE

→

HaltState
intercepts

→

Policy check:
destructive + prod

→

DENIED

→

Agent receives
denial + reason

Blocked in under 50ms. No data lost. Full audit trail preserved.

📜

The Evidence

Every blocked action is recorded with full context:

{ "action": "database.execute", "agent_id": "data-cleanup-agent", "decision": "DENIED", "policy_id": "pol-no-destructive-prod", "params": { "operation": "DROP TABLE", "target": "customers", "environment": "production" }, "reason": "Destructive operations blocked on production databases", "event_hash": "b4c3d9e0f1a2..." }

Security team can review attempted destructive actions and investigate potential issues.

AI Agent Governance for Data Operations

The Risk

The Policy

The Flow

The Evidence

Protect Your Data Infrastructure