From Principles to Operable Governance
Governance only matters if it can be applied to real systems.
INTEKNIQUE frameworks define how AI-enabled systems must behave to remain acceptable inside regulated environments—not just how they are documented, approved, or described.
These frameworks operate together as a layered model, moving from abstract principles to continuous operational control.
The Layered Model
INTEKNIQUE governance is expressed through three tightly coupled frameworks:
- Model Context Protocol (MCP)
- Validation Context Protocol (VCP)
- Continuous Autonomous Validation Execution Structure (CAVES)
Each addresses a different regulatory failure mode introduced by AI systems.
Together, they form a complete governance stack.
Model Context Protocol (MCP)
AI systems do not operate in isolation.
They operate within context—assumptions, constraints, goals, data boundaries, and decision rules.
Model Context Protocol defines:
- What context an AI system is allowed to access
- How that context is represented and constrained
- How assumptions are made explicit and inspectable
- How changes in context are detected and governed
Without explicit context control, AI behavior cannot be explained or defended.
MCP ensures that system behavior is grounded in declared, auditable context rather than implicit or emergent assumptions.
Validation Context Protocol (VCP)
A framework for treating validation as a continuously evaluated operational state.
VCP reframes validation as a living context, not a one-time event.
This enables regulated organizations to answer a critical question at any moment:
Is the system currently operating
within acceptable boundaries?
Continuous Autonomous Validation Execution Structure (CAVES)
As AI systems become more autonomous, validation cannot rely solely on human oversight.
CAVES defines the structural requirements for:
- Continuous monitoring of system behavior
- Automated detection of boundary violations
- Evidence generation triggered by system events
- Escalation and intervention mechanisms when conditions change
Rather than validating systems after the fact, CAVES enables systems to participate in their own validation.
This allows organizations to move from retrospective justification to real-time regulatory defensibility.
How the Frameworks Work Together
- MCP defines what the system knows and assumes
- VCP defines whether the system is operating acceptably
- CAVES defines how that acceptability is continuously enforced and evidenced
Individually, each framework addresses a specific governance gap.
Together, they enable AI systems to operate inspectably, accountably, and sustainably inside regulated environments.
What These Frameworks Are Not
These frameworks are not:
- AI tools
- Automation platforms
- Vendor solutions
- Prescriptive implementations
They are governance abstractions designed to be:
- Technology-agnostic
- Platform-independent
- Regulator-facing
- Implementation-aware without being product-bound
From Frameworks to Practice
INTEKNIQUE evaluates these frameworks through internal reference systems—non-commercial implementations designed to test how governance concepts translate into real system behavior.
Frameworks are maintained in GitHub repositories that are open to peer review, experimentation, and contribution by practitioners interested in advancing regulatory-viable AI.
These implementations exist to:
- Validate feasibility
- Surface regulatory edge cases
- Demonstrate operational alignment
- Provide inspectable examples
They are not the offering.
They are the proof.
Frameworks do not slow innovation.
They make it survivable.