DriftGuard

1. Executive Summary

The Core Problem: Knowledge is not static. Rules evolve, categories drift, and evidence accumulates, but AI models often treat data as immutable facts. When the world changes but the model’s knowledge base does not, the system suffers from "Silent Decay." It continues to act confidently on outdated premises, leading to liability and operational failure. The Solution: DriftGuard is an operational safety middleware. It combines Drift Detection (monitoring data freshness and validity), AuditTrace (recording reasoning steps), and Circuit Breakers (automated refusal). The Goal: To ensure the system remains "Legible, Bounded, and Corrigible", protecting the organization by freezing actions when reality diverges from the model's training.

2. User Personas

• The Risk Officer: Needs to prove why an AI agent authorized a transaction six months ago for legal defense.

• The Systems Operator (SRE): Needs an automated "Kill Switch" to stop a runaway agent if an external API (e.g., pricing) begins returning errors.

• The Knowledge Manager: Needs alerts when "Authoritative Knowledge" (Layer 1) conflicts with new "Observed Reality" (Layer 2),.

3. Core Value Proposition

• Prevent "Zombie" Logic: Stops the AI from applying last year’s rules to today’s context.

• Legal Defensibility: Provides an immutable record of the exact knowledge and confidence levels used at the moment of decision.

• Bounded Responsibility: Enforces the "Right to Refuse," ensuring the system stops when it detects it is unsafe.

4. Functional Requirements

4.1. The Drift Monitor (Data Freshness)

• Requirement: The system must monitor the Validity Windows of all data retrieved for a decision.

• Trigger: If a data point used in reasoning has exceeded its Time-To-Live (TTL) or validity date.

• Action: Flag the inference as "High Risk / Stale."

• Rationale: "Systems that treat knowledge as static documents degrade silently".

4.2. Automated Circuit Breakers (The Kill Switch)

• Requirement: Configurable thresholds for failure rates and confidence decay that trigger an immediate freeze on specific capabilities.

• Mechanism: If the "Refusal Rate" or error rate for a specific intent (e.g., execute_refund) exceeds X, the Circuit Breaker opens.

• Output: The system instantly revokes the execute_refund capability from the AI's "Intent Surface," forcing a fallback.

• Rationale: "Autonomy without structure... accumulates risk invisibly until failure is unavoidable".

4.3. AuditTrace (The Black Box)

• Requirement: For every "Irreversible Action", the system must log a cryptographic snapshot containing:

    1. The Input Query.

    2. The Specific Knowledge Version used (e.g., Policy_v4).

    3. The Confidence Score generated.

    4. The Logic Path/Justification cited.

• Rationale: When disputes arise, questions like "Who authorized the action?" must be answerable. Systems without this traceability are "indefensible".

4.4. Outcome Feedback Loop

• Requirement: The system must ingest post-action data (e.g., "Was the refund accepted?") to compare Prediction vs. Reality.

• Drift Detection: If the model predicts "Success" but reality returns "Failure" consistently, DriftGuard alerts that the underlying logic has drifted.

5. Lab: Graceful Degradation Protocol

Requirement: Design a protocol for how the system behaves when DriftGuard triggers a "Freeze." Context: "A degraded system that communicates clearly preserves trust far better than a confident system that is wrong".

Protocol Name: "The Advisory Fallback"

Scenario: DriftGuard detects that the "Inventory API" is returning data older than 24 hours (Drift Detected).

Stage 1: The Circuit Break

• Action: DriftGuard intercepts the check_stock intent.

• Status: Sets capability to READ_ONLY.

Stage 2: The User Communication (Graceful Degradation)

• The Wrong Way (Silent Failure): The AI guesses, "It is likely in stock."

• The DriftGuard Way (Honest Fallback): The AI receives the constraint and responds: "I cannot confirm immediate availability right now because our inventory data is updating. I can place this on hold for you, but I cannot confirm shipping until tomorrow."

• Principle: "Explain instead of act. Ask instead of assume".

Stage 3: The Recovery

• Action: Once data freshness returns to <1 hour, the Circuit Breaker closes, restoring the buy_now capability.

6. Legal Defensibility Strategy

Objective: To maintain liability protection for automated decisions.

1. Immutable Versioning: Every decision is linked to a specific version of the Ontology and Ruleset. We can prove that at the time of the decision, the AI followed the correct policy v3.2.

2. Uncertainty Logs: We log that the AI expressed "85% confidence." If the user proceeds despite the warning, the liability shifts to the user. "Explicit uncertainty... supports trust calibration".

3. Refusal Records: We log every instance where the system refused to act. This proves the system has active safety boundaries and is not "negligently autonomous".

7. Success Metrics (KPIs)

1. Drift Identification Rate: How quickly does the system alert on "stale" knowledge before a user complains? (Target: <10 minutes).

2. Defensibility Score: Can we reconstruct the exact reasoning state of a transaction from 90 days ago within 5 minutes? (Target: 100%).

3. False Positive Freeze: How often did the Circuit Breaker activate unnecessarily? (Target: <1%).