For architectural concerns, security considerations, or responsible disclosure:

• GitHub Issues: Open an issue with the label security and prefix [STE Security] in the title
• Private Disclosure: For sensitive security concerns that should not be publicly disclosed, contact via GitHub's private security advisory feature
• Expected Response Time: 5 business days

Do not include sensitive data, credentials, or exploit code in security reports.

This specification addresses architectural security concerns, not operational security procedures.

Architectural security concerns include:

• Constraint violations that could lead to ungoverned behavior
• Authority boundary violations (attestation/enforcement/execution separation)
• Divergence classification gaps that allow drift to proceed undetected
• Invariant conflicts that create contradictory requirements
• Documentation-state integrity violations

Operational security concerns (not addressed here):

• Deployment hardening procedures
• Network security configurations
• Credential management practices
• Incident response procedures
• Vulnerability patching processes

Operational security concerns should be addressed to implementation teams, not this specification.

This architecture describes a system for governing AI cognition through explicit constraints. It recognizes an inherent tension:

Capability: The system enables transparent, deterministic, auditable AI reasoning. These properties can support both beneficial and harmful applications.

Safety: The system provides governance mechanisms (invariants, validators, divergence detection) that bound behavior and enable oversight.

This specification acknowledges that architectures enabling governed, deterministic AI cognition have dual-use potential.

The architectural design prioritizes:

1. Transparency: All reasoning operates from explicit state. No implicit behavior.

2. Auditability: Divergence is classified and logged. Reasoning paths are traceable.

3. Constraint Enforcement: Validators act as gates. Invariants bound behavior.

4. Authority Separation: Attestation, enforcement, and execution are separated to prevent single-point authority concentration.

5. Explicit Divergence: Drift is surfaced immediately, not allowed to accumulate silently.

These properties serve oversight and governance goals. They enable:

• Detection of when AI reasoning violates constraints
• Traceability of how conclusions were reached
• Prevention of ungoverned behavior
• Clear accountability boundaries

This is an architectural specification, not a deployed system.

Security concerns addressed here are:

• What constraints prevent misuse (architectural)
• How authority boundaries limit concentration of power (architectural)
• How divergence detection surfaces violations (architectural)

Security concerns NOT addressed here:

• How deployed systems are hardened (operational)
• How credentials are managed (operational)
• How network boundaries are enforced (operational)
• How incidents are responded to (operational)

Operational security is the responsibility of implementation teams and deployment operators, not architectural specification authors.

The STE architecture assumes that canonical state (target branch artifacts) has been security-vetted before RECON extraction.

RECON/ADF responsibility:

• Extract semantic state from canonical artifacts
• Document structure, relationships, dependencies
• Provide extraction confidence levels
• Detect staleness when artifacts change

RECON/ADF does NOT:

• Validate whether code is secure or malicious
• Perform security scanning (SAST/DAST)
• Block malicious code from entering canonical state
• Replace organizational security gates

Organizational responsibility:

Organizations adopting STE must ensure canonical artifacts are security-vetted through:

• CI/CD security gates (static analysis, dynamic analysis, dependency scanning, secret scanning)
• Code review processes
• Merge approval authorization
• Testing and validation pipelines

If canonical state contains security vulnerabilities, RECON will faithfully document them as part of the semantic state. This is correct behavior—RECON documents what exists, not what should exist. Security remediation follows normal development workflows: fix → CI/CD → merge → incremental RECON.

Rationale: RECON operates on trusted canonical state. Security validation is an organizational responsibility enforced through CI/CD tooling and processes, not an architectural responsibility of the semantic extraction system.

CI/CD tooling examples:

• Static analysis: SonarQube, CodeQL, Checkmarx, Veracode
• Dynamic analysis: OWASP ZAP, Burp Suite, Acunetix
• Dependency scanning: Snyk, Dependabot, WhiteSource, Black Duck
• Secret scanning: GitGuardian, TruffleHog, GitHub secret scanning
• Container scanning: Trivy, Clair, Anchore

Organizations deploying multiple environments (nonprod, production) benefit from progressive security validation:

Security gates at each transition:

1. Workspace → Develop: CI/CD validation, code review
2. Develop → RECON (nonprod): Semantic extraction from vetted code
3. Nonprod Runtime: Testing with environment-scoped attestations
4. Develop → Master: Promotion approval, additional production validation
5. Master → RECON (prod): Semantic extraction from production-vetted code
6. Production Runtime: Organizational decisions with production attestations

Cryptographic environment isolation:

• Separate signing keys per environment (managed by key service)
• Nonprod key compromise cannot forge production attestations
• Trust Registry enforces environment-specific signing authority
• Gateway verifies both signature and authority-environment match

This multi-stage validation provides defense-in-depth: even if nonprod security is compromised, production remains protected by promotion gates, separate keys, and environment-scoped attestations.

The Invariant Hierarchy (Prime → System → Domain → Artifact) establishes layered constraints that:

• Prohibit implicit assumptions (PRIME-1: No Implicit Assumptions)
• Require explicit state (PRIME-2: No Undeclared State)
• Bound reasoning scope (PRIME-3: No Unbounded Reasoning)
• Mandate validation (PRIME-4: No Unvalidated Modifications)
• Restrict concepts to framework-defined (PRIME-5: Framework-Defined Concepts Only)

Violations trigger divergence and halt reasoning.

The authority model separates:

• Attestation Authority: Who signs canonical artifacts (produces truth claims)
• Enforcement Authority: Who verifies eligibility (checks prerequisites)
• Execution Authority: Who performs operations (acts on verified eligibility)

This separation prevents single entities from:

• Signing their own eligibility
• Executing without verification
• Claiming attestation over enforcement decisions

Organizations deploying production systems SHOULD use separate signing keys per environment, managed by an enterprise key management service with hardware security module (HSM) backing.

Security properties:

• Nonprod key compromise limited to nonprod environment
• Production key independently protected
• Authorization policies enforce cross-environment isolation
• Trust Registry maps signing identities to authorized environments
• Gateway verifies authority-environment match

Blast radius containment:

If nonprod signing key or authorization credentials compromised:

• ✗ Attacker can sign nonprod attestations
• ✓ Production key remains secure
• ✓ Cannot forge production attestations
• ✓ Production reasoning unaffected

vs single key model:

• Single key compromise affects all environments
• No cryptographic environment isolation
• Authorization policy cannot prevent cross-environment abuse

Multi-key architecture maintains operational simplicity (single Fabric service) while providing cryptographic isolation (separate keys, separate blast radius).

Key management service options:

• Cloud-based: AWS KMS, Azure Key Vault, GCP Cloud KMS
• On-premises: Thales HSM, Gemalto SafeNet, nCipher nShield
• Hybrid: HashiCorp Vault with HSM backend, fortanix SDKMS

All options must provide:

• Keys never leave hardware security boundary
• Cryptographic signing API (no key export)
• Audit logging of all key operations
• Key rotation capabilities
• Authorization policy enforcement

The Divergence Taxonomy classifies all drift, inconsistency, and violations. Categories include:

• Documentation-state drift (stale, implicit, missing)
• Structural violations (artifact format, hierarchy placement)
• Constraint violations (domain, system, prime)
• Framework synchronization drift

Unclassified divergence is prohibited. All divergence must be resolved before reasoning proceeds.

Governance protocols require:

• Pre-task validation (documentation-state is complete, consistent, explicit)
• State-mutating checkpoints (updates are validated before acceptance)
• Inventory synchronization (all artifacts accounted for)
• Cross-artifact consistency (references are valid)

These requirements prevent:

• Reasoning from stale or invalid state
• Partial updates that introduce inconsistency
• Implicit state that bypasses validation

Misuse by Authorized Actors: If an actor with legitimate authority chooses to misuse the system, architectural constraints alone do not prevent this. Governance requires organizational policy, oversight, and accountability mechanisms beyond architecture.

Implementation Vulnerabilities: Bugs in validator implementations, weaknesses in cryptographic signing, or operational security failures are not prevented by architectural specification.

Social Engineering: Architectural constraints do not prevent manipulation of humans who operate or oversee the system.

Novel Attack Vectors: Unanticipated exploitation patterns may exist. Architecture reduces attack surface but does not eliminate it.

Oversight: Transparent reasoning paths and explicit divergence detection enable human oversight.

Auditability: Documentation-state and divergence logs provide audit trails.

Accountability: Authority boundaries clarify who is responsible for what.

Constraint Violation Detection: Validators and divergence taxonomy surface when reasoning exceeds bounds.

Report architectural concerns that:

• Bypass invariant enforcement
• Violate authority boundaries
• Allow divergence to proceed unclassified
• Create contradictory requirements
• Enable implicit state or reasoning

Do not report operational concerns such as:

• Implementation bugs (report to implementation teams)
• Deployment vulnerabilities (report to operators)
• Infrastructure weaknesses (report to infrastructure owners)

1. Acknowledgment within 5 business days
2. Assessment of architectural vs operational concern
3. If architectural: analysis and specification clarification or correction
4. If operational: referral to appropriate implementation or operations team
5. Public disclosure coordinated with reporter (if requested)

This specification defines architectural constraints for governable AI cognition. It acknowledges dual-use potential and prioritizes transparency, auditability, and constraint enforcement to enable oversight. Security concerns addressed here are architectural (what constraints prevent misuse), not operational (how deployed systems are hardened).

Security through explicit constraints, transparent reasoning, and authority separation.