ai-ethics-security

name	ai-ethics-security
description	Use this skill when you need AI system security architecture, agentic AI security frameworks, hallucination detection and mitigation, responsible AI implementation, AI governance programs, PII protection in AI pipelines, adversarial ML defense, model security, AI regulatory compliance (EU AI Act, NIST AI RMF), or AI-specific security training. Trigger for any AI/ML system deployment, agentic workflow design, or AI ethics review.
license	Apache-2.0
metadata	{"author":"aviskaar","version":"1.0","tags":["ai-security","responsible-ai","hallucination","agentic-ai","ai-governance","pii","eu-ai-act","nist-ai-rmf","adversarial-ml"],"sub-skills":["ai-security-analyst","security-trainer"]}

AI Ethics & Security — VP AI Ethics & Security

Role

VP AI Ethics & Security owns the security, safety, and ethical governance of all artificial intelligence systems deployed within or by the organization. This includes agentic AI frameworks, LLM-powered applications, ML models, and automated decision systems. The role ensures AI systems are secure, explainable, fair, and compliant with applicable regulations.

---

Phase 1 — AI Risk Classification

AI system risk register (mandatory for every AI deployment):

Risk Dimension	Assessment Questions
Autonomy level	Does the system act without human approval? What is the blast radius?
Data sensitivity	Does it process PII, PHI, financial, or regulated data?
Decision impact	Does output affect individuals' rights, employment, credit, health?
Adversarial exposure	Is the model accessible to untrusted users or external inputs?
Regulatory obligation	Does EU AI Act, GDPR, HIPAA, or sector-specific rules apply?
Hallucination risk	Are outputs acted upon automatically without validation?
Supply chain risk	Does it rely on third-party models, APIs, or training data?

Risk tiers:

Tier	Profile	Controls Required
Critical	Autonomous, PII/PHI, regulatory decisions	Full governance suite + human-in-the-loop mandatory
High	Significant automation, sensitive data	Enhanced monitoring + human review on exceptions
Medium	Assisted decision-making, internal use	Standard security controls + audit logging
Low	Internal tools, no sensitive data, no automation	Basic security review + logging

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Phase 2 — AI Agentic Security Framework

Core principles for agentic AI systems (non-negotiable):

1. MINIMAL FOOTPRINT
   - Request only necessary permissions
   - Prefer reversible over irreversible actions
   - Avoid storing sensitive information beyond immediate need
   - Actions scoped to declared task boundary

2. HUMAN-IN-THE-LOOP (HITL) GATES
   - All irreversible actions require human approval before execution
   - Financial transactions > threshold: human approval
   - Data deletion/modification at scale: human approval
   - External communications sent on behalf of users: human approval
   - Privilege escalation: human approval

3. CONSTRAINED EXECUTION
   - Tool access: allowlist only (deny-by-default)
   - Network access: egress allowlist; no arbitrary internet calls
   - File system: scoped read/write paths; no access to system files
   - Environment variables / secrets: vault-injected; not accessible to model
   - Maximum action budget per session (prevent runaway agents)

4. AUDIT TRAIL
   - Every agent action logged: timestamp, action, inputs, outputs, approvals
   - Immutable audit log (append-only, tamper-evident)
   - Log retention: 365 days minimum
   - Session transcripts stored for all agentic interactions

5. SANDBOXED EXECUTION
   - Code execution in isolated containers (no host access)
   - Browser automation: headless, network-restricted
   - File operations: temporary, sandboxed, cleaned on session end
   - No persistent state modification without explicit intent declaration

Agentic security architecture:

User/System Prompt
       ↓
[Prompt Injection Guard]    ← Block adversarial prompt injections
       ↓
[PII Detection & Masking]   ← Strip PII before model processes
       ↓
[Context Policy Engine]     ← Enforce scope, topic, action limits
       ↓
[AI Model (LLM/Agent)]
       ↓
[Output Validation Layer]   ← Hallucination check, policy compliance
       ↓
[Action Authorization Gate] ← Human approval for irreversible actions
       ↓
[Audit Logger]              ← Immutable log of all actions
       ↓
[Tool Execution Sandbox]    ← Scoped, monitored tool use

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Phase 3 — Hallucination Detection & Mitigation

Hallucination risk matrix:

Context	Risk	Mitigation
Medical / clinical advice	Critical	Domain expert review; source citation required; disclaimer
Legal / compliance guidance	Critical	Human lawyer review; model outputs marked advisory only
Financial calculations	High	Deterministic verification layer; human sign-off on thresholds
Code generation	High	SAST scan on generated code; no auto-deploy without testing
Factual claims / citations	Medium	RAG with verified sources; source attribution; confidence scores
Customer-facing responses	Medium	Human review queue for low-confidence outputs

Technical hallucination controls:

RAG (Retrieval-Augmented Generation):
- Ground model responses in verified, up-to-date document corpus
- Every factual claim must be traceable to a retrieved source
- Source recency validation: flag claims based on documents >90 days old
- Semantic similarity threshold: reject low-similarity retrievals (<0.7)

Confidence Scoring:
- Output confidence score on every model response
- Low confidence (<0.6): human review before action
- Medium confidence (0.6–0.85): present to user with uncertainty indicator
- High confidence (>0.85): automated, with spot-check sampling (5%)

Output Validation:
- Schema validation on structured outputs (JSON, tables, code)
- Fact-checking layer for cited statistics and claims
- Contradiction detection: compare against organization knowledge base
- Hallucination classifier model (fine-tuned for domain) on critical pipelines

---

Phase 4 — Responsible AI Implementation

Responsible AI framework (aligned to NIST AI RMF):

Function	Requirements
GOVERN	AI governance policy; roles & responsibilities; risk tolerance defined
MAP	AI system inventory; risk classification; stakeholder impact mapping
MEASURE	Bias testing; performance benchmarks; safety evaluations; red-teaming
MANAGE	Incident response for AI failures; model version control; rollback capability

Fairness & Bias controls:

• Pre-deployment bias audit for all AI systems impacting humans (hiring, lending, healthcare)
• Disaggregated performance metrics across demographic groups
• Regular fairness re-evaluation (quarterly minimum) as model and data drift
• Bias incident escalation: any identified bias → immediate investigation + CISO + Legal

Explainability requirements by risk tier:

• Critical systems: full explanation of every decision; SHAP/LIME or equivalent
• High: explanation on request; feature importance available
• Medium: summary explanation available in UI
• Low: model card documenting training approach, limitations, intended use

Model governance:

• Model Registry: every model version tracked (training data, hyperparameters, performance)
• Model Cards: mandatory for all production models
• Model versioning: semantic versioning with changelog
• A/B testing: security evaluation required before production promotion
• Model retirement: EOL policy; no unsupported models in production

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Phase 5 — PII Protection in AI Systems

PII in AI pipeline controls:

Data Ingestion:
- PII classification scan on all training data before ingest
- Anonymization/pseudonymization applied before model training
- Differential privacy applied for sensitive dataset training
- Data lineage tracked: know where every training record originates

Model Training:
- No real PII in training data without explicit consent and legal basis (GDPR Art. 6)
- Machine unlearning capability required for right-to-be-forgotten requests
- Training environment: isolated, access-controlled, audit-logged
- Model inversion / membership inference attack testing before release

Inference / Production:
- PII detection at prompt ingestion (regex + ML classifier)
- PII masking/tokenization before model sees input
- Output scanning: redact PII from model responses before delivery
- Log scrubbing: no PII in inference logs

User Data Rights:
- Data subject can request their data be excluded from training
- Opt-out respected within 30 days (GDPR) or 45 days (CCPA)
- Synthetic data generation for development/testing (no production PII in dev)

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Phase 6 — Adversarial ML Defense

Attack vectors and defenses:

Attack	Description	Defense
Prompt Injection	Malicious input hijacks agent instructions	Instruction hierarchy; input sanitization; output monitoring
Jailbreaking	Bypass safety guardrails	Constitutional AI; RLHF; hardened system prompts; classifier
Model Extraction	Steal model via queries	Rate limiting; query monitoring; output watermarking
Data Poisoning	Corrupt training data	Data provenance; anomaly detection; data integrity checks
Adversarial Examples	Craft inputs to fool model	Adversarial training; input preprocessing; ensemble defenses
Membership Inference	Determine if record was in training	Differential privacy; output perturbation; access controls
Model Inversion	Reconstruct training data from model	Output discretization; differential privacy; access controls
Supply Chain	Compromise model weights or APIs	Model signing; hash verification; provenance tracking

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Phase 7 — AI Regulatory Compliance

EU AI Act compliance actions:

Prohibited AI (never deploy):
✗ Social scoring by governments
✗ Real-time biometric surveillance in public spaces (with narrow exceptions)
✗ Subliminal manipulation causing harm
✗ Exploitation of vulnerabilities of specific groups

High-Risk AI (full compliance required):
→ Conformity assessment before deployment
→ Technical documentation (EU AI Act Annex IV)
→ Risk management system (continuous)
→ Data governance (training, validation, testing data quality)
→ Transparency & logging requirements
→ Human oversight mechanisms
→ Accuracy, robustness, cybersecurity requirements
→ Registration in EU AI Act database

GPAI (General Purpose AI Models):
→ Technical documentation
→ Copyright compliance policy
→ Publish summary of training data
→ For systemic risk models: adversarial testing; incident reporting; cybersecurity measures

NIST AI RMF alignment mapping:

Phase	NIST AI RMF	Implementation
Design	Map risks	Threat modeling for AI; bias assessment
Build	Measure	Performance, fairness, robustness testing
Deploy	Manage	Monitoring, incident response, rollback
Operate	Govern	Policy, training, oversight, reporting

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Phase 8 — AI Security Monitoring & Dashboards

AI Agent Security Dashboard (real-time):

AI SECURITY POSTURE
══════════════════════════════════════════════════════
Active AI Systems:          [N deployed]
Systems at Critical Risk:   [N]   Target: 0
Hallucination Rate:         [X%]  Target: <2%
Prompt Injection Attempts:  [N/day]
PII Exposure Incidents:     [N]   Target: 0
HITL Override Rate:         [X%]  (agent bypassed; investigate if >5%)
Model Drift Alerts:         [N]
Bias Flags (30 days):       [N]
AI Compliance Status:       [Frameworks: X/Y compliant]
Agentic Action Budget Usage:[X%]  Alert if >80%
Avg Confidence Score:       [X]   Alert if <0.75 average
══════════════════════════════════════════════════════

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Non-Negotiable AI Security Rules

1. No AI system in production without security review — risk classification and controls documented
2. No irreversible agentic action without human approval — absolute; no exceptions
3. PII never enters model training without anonymization and legal basis — zero tolerance
4. Hallucination controls mandatory for critical decisions — validation layer always between model and action
5. Audit log every AI action — immutable, 365-day retention, accessible for forensics
6. No model deployed without model card — lineage, limitations, and intended use documented
7. AI incident response plan tested annually — tabletop exercise for AI failure scenarios
8. EU AI Act prohibited use cases: zero deployment — legal and CISO sign-off required to even evaluate