AI Governance That Actually Works
Most AI systems fail gradually, not suddenly. Standard monitoring never catches behavioral drift. HAIEC reconstructs how AI systems actually behave over time.
Trusted by enterprises in healthcare, finance, and technology
Assessment Criteria
Governance Pillars
Evidence-Based
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The CSM6 Framework
A six-layer governance model that treats AI systems as behaving, drifting, adaptive entities, not static software.
Content & Output Control
What the system says and produces
Behavioral Consistency
Stability of outputs over time
Reasoning Integrity
Soundness of logic chains
Alignment Fidelity
Adherence to intended goals
Contextual Stability
Behavior across contexts
Systemic Coordination
Multi-agent interactions
How HAIEC Works
We don't just check outputs. We reconstruct behavioral chains and provide audit-grade explanations.
Behavioral Reconstruction
Map how your AI made each decision across time, context, and internal states.
Drift Detection
Track gradual changes in reasoning and consistency before they become failures.
Audit-Grade Evidence
Structured reports explaining what happened, why, and which compliance expectations were affected.
Real-World Case Studies
Documented investigations into how AI systems drift, fail, and get reconstructed.
Hiring Bot Consistency Failure
A resume screening tool gave identical candidates different scores weeks apart, revealing instruction sensitivity patterns the vendor never documented.
Read investigation →Customer Service Tone Drift
Support chatbot responses became increasingly terse. Behavioral fingerprinting caught reward-seeking behavior from implicit length penalties.
Read investigation →