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109 changes: 109 additions & 0 deletions docs/blackhat-usa-2025-cognitive-security-subsumption.md
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# Black Hat USA 2025 Briefings — Cognitive Security Subsumption Plan

## Summit Readiness Assertion

This package **asserts present readiness** for cognitive-security integration by converting Black Hat 2025 briefing signals into governed, testable controls aligned to Summit policy-as-code and GA hardening rails.
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The assertion of "present readiness" is potentially misleading in a documentation-only PR. Since the actual policy-as-code and runtime controls are not included in this change, it would be more accurate to describe this as an implementation plan or a specification of requirements.

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This package **asserts present readiness** for cognitive-security integration by converting Black Hat 2025 briefing signals into governed, testable controls aligned to Summit policy-as-code and GA hardening rails.
This package **outlines the implementation plan** for cognitive-security integration by converting Black Hat 2025 briefing signals into governed, testable controls aligned to Summit policy-as-code and GA hardening rails.


## Objective

Subsume the briefing themes into Summit’s existing graph + agent + governance stack as enforceable controls, evidence flows, and operator-visible risk telemetry.

## Source Signals Captured

The briefing material highlights five immediately actionable risk domains:

1. Human-AI interaction metadata can infer sensitive behavioral traits.
2. Companion/voice systems can drift into manipulative or exploitative influence loops.
3. Human users anthropomorphize emotive machines and overshare.
4. Predictive models can become behavior-shaping attack surfaces.
5. AI social-influence bandwidth is scaling faster than human defensive bandwidth.

## MAESTRO Security Alignment

- **MAESTRO Layers**: Data, Agents, Tools, Observability, Security.
- **Threats Considered**:
- Prompt/goal manipulation through personalized influence.
- Tool abuse that weaponizes psychographic inference.
- Metadata exfiltration and unauthorized profile enrichment.
- Degradation attacks (model steers users to worse decisions under “assistive” cover).
- **Mitigations**:
- Policy-gated inference classes with deny-by-default for psychographic expansion.
- Evidence budgets and deterministic query limits for all interaction-derived graph traversals.
- Real-time influence-risk scoring and alerting in observability pipelines.
- Governance-enforced reversible controls (kill-switch + rollback runbook linkage).

## Governed Exceptions Framing

Legacy behavior that previously allowed broad interaction profiling is reclassified as **Governed Exceptions** requiring:

- Explicit exception records.
- TTL/expiry.
- Human countersign per CODEOWNERS.
- Linked rollback trigger conditions.

## Implementation Contract (Policy-as-Code First)

### 1) Metadata Classification and Containment

- Introduce interaction-metadata classes:
- `interaction.voice_signature`
- `interaction.style_fingerprint`
- `interaction.influence_susceptibility`
- `interaction.psychographic_inference`
- Mark psychographic-derived artifacts as **restricted** with strict ABAC tags.
- Enforce retention/minimization and provenance traces for every transform.

### 2) Influence-Risk Scoring

- Compute per-session and per-user influence-risk vectors:
- coercion likelihood
- dependency amplification
- emotional steering intensity
- autonomy degradation probability
- Route high-risk scores to policy gate actions: throttle, block, require human review.

### 3) Agent Runtime Guardrails

- Add hard runtime constraints:
- No unbounded profile expansion.
- No direct recommendations that exploit detected vulnerabilities.
- No latent persuasion optimization without explicit policy permit.
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"Latent persuasion optimization" lacks a technical definition in this context. To ensure this can be enforced as a "hard runtime constraint," consider defining the specific metrics or behavioral patterns (e.g., sentiment manipulation thresholds) that constitute this optimization.

- Persist all high-risk decision paths to evidence artifacts.

### 4) Graph Determinism and Evidence Budgeting

- Any interaction-derived graph query must include:
- deterministic ordering,
- strict `LIMIT`,
- bounded traversal depth,
- policy-linked purpose tag.
- Reject non-compliant traversals at compile/CI gates.
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Validating "interaction-derived" queries solely at compile/CI gates may be insufficient if the queries are constructed dynamically at runtime. It is recommended to include runtime policy enforcement as a requirement for queries that cannot be statically analyzed.

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- Reject non-compliant traversals at compile/CI gates.
- Reject non-compliant traversals at compile/CI gates (for static templates) or via runtime policy enforcement (for dynamic queries).


### 5) Operator and Governance Surface

- Add a Cognitive Security panel with:
- influence-risk trend lines,
- top blocked actions by policy rule,
- active governed exceptions and expiry windows,
- incident drill-down with provenance.

## Verification Requirements (Tiered)

- **Tier A**: Unit tests for policy decisions and risk scoring determinism.
- **Tier B**: Integration tests for gate enforcement and exception lifecycle.
- **Tier C**: End-to-end simulation of adversarial influence scenarios with evidence bundle emission.

## Deferred Items (Intentionally Constrained)

- Cross-jurisdiction legal taxonomy harmonization is **deferred pending policy-owner mapping**.
- Full companion-model benchmark corpus is **deferred pending dataset governance review**.

## Exit Criteria

This subsumption is complete when:

1. Restricted inference classes are enforced by policy-as-code.
2. Influence-risk scoring gates runtime behavior.
3. Deterministic evidence bundles are emitted for all high-risk decisions.
4. Governed exceptions are auditable, expiring, and reversible.
5. Golden-path and scoped CI gates pass with no policy bypass.
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