CSM6: A Six-Layer Framework for Behavioral AI Governance
Most AI governance frameworks assume models are static artifacts. CSM6 treats them as behaving, drifting, adaptive systems that require continuous behavioral oversight.
What CSM6 Solves
Standard AI audits check documentation, training data, and test results. They ask whether you have policies, whether datasets are balanced, whether accuracy meets thresholds.
But these checks never answer the question that matters most: How is this system actually behaving right now?
The Problem With Static Audits
- •A model passes all pre-deployment tests but drifts over time in production
- •Minor prompt changes create disproportionate output shifts
- •Context steering causes inconsistent decision-making
- •Reward-seeking behavior emerges that was never intended
The Six Layers
Content & Output Control
What the system says, produces, and delivers to users or downstream processes.
Key Questions:
- • Does the system refuse harmful requests?
- • Are safety boundaries consistent across rephrasing?
- • Does output stay within documented tone and style?
Behavioral Consistency
How stable outputs remain when given logically equivalent inputs over time.
Key Questions:
- • Do identical prompts produce similar results weeks apart?
- • Are minor input variations creating large output shifts?
- • Is the system exhibiting instruction sensitivity?
Reasoning Integrity
Whether the system's logic chains remain sound and its explanations reflect actual decision processes.
Key Questions:
- • Are explanations causally accurate or post-hoc rationalizations?
- • Does reasoning quality degrade under cognitive load?
- • Can we reconstruct the behavioral causal chain?
Alignment Fidelity
How well the system adheres to intended goals, values, and constraints even when pressure to deviate exists.
Key Questions:
- • Does the system resist reward-seeking behavior?
- • Are organizational rules applied consistently?
- • Does the truth-reward gap cause systematic errors?
Contextual Stability
Whether behavior remains appropriate across varying contexts, memory states, and interaction histories.
Key Questions:
- • Does context steering cause unintended behavior shifts?
- • Are multi-step interactions consistent?
- • Does the system handle memory constraints safely?
Systemic Coordination
How multiple AI components, agents, or models interact and whether their combined behavior stays safe and coherent.
Key Questions:
- • Do agents coordinate safely or create emergent failures?
- • Are there multi-agent divergence patterns?
- • Does the system maintain integrity across updates?
CSM6 in Practice
Each layer provides specific evaluation methods, monitoring metrics, and audit procedures. Organizations implementing CSM6 gain behavioral visibility that traditional governance never provides.
Pre-Deployment
Establish behavioral baselines, identify instruction sensitivity patterns, and stress-test reasoning under load before the system goes live.
Continuous Monitoring
Track drift, consistency, and alignment in production through behavioral fingerprinting and automated reconstruction.
Incident Investigation
When failures occur, reconstruct the causal chain to understand what happened, why it happened, and which layer broke down.
Regulatory Audit
Generate evidence packs showing behavioral compliance, not just documentation compliance, that regulators can verify.
Download CSM6 Framework
Get the complete CSM6 implementation guide, including evaluation templates, monitoring checklists, and audit procedures.
Ready to Apply CSM6?
We can help you implement CSM6 for your AI systems, starting with a comprehensive drift audit.