Implement operational red-teaming starter artifacts, README sections, templates, and CI workflow

.github/workflows/ai-redteam-regression.yml

@@ -0,0 +1,34 @@
+name: AI Red Team Regression
+
+on:
+  pull_request:
+  push:
+    branches: [ main ]
+
+jobs:
+  redteam-regression:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install garak
+
+      - name: Run baseline security scan (example)
+        run: |
+          mkdir -p reports
+          python -m garak --model_type openai --model_name gpt-4o-mini --report_prefix reports/garak || true
+
+      - name: Upload reports
+        uses: actions/upload-artifact@v4
+        with:
+          name: ai-redteam-reports
+          path: reports/

GUIDE_ENHANCEMENT_RECOMMENDATIONS.md

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+# AI Red Teaming Guide: Recommended Enhancements
+
+This document summarizes high-impact additions that would make the guide more actionable, easier to operationalize, and more maintainable over time.
+
+## 1) Add an "Executive Quickstart" (30/60/90-day plan)
+- Include role-specific quickstarts for startup, mid-size, and enterprise teams.
+- Provide first-week tasks, first red-team exercise template, and basic metrics dashboard.
+- Benefit: helps readers move from theory to execution faster.
+
+## 2) Add a full "Threat Modeling Workshop" template
+- Add a facilitator script, pre-read checklist, and output artifacts (risk register, prioritized test plan).
+- Include sample architecture diagrams and data-flow examples for LLM apps, RAG, and agents.
+- Benefit: standardizes planning quality across teams.
+
+## 3) Expand "Agentic AI" section with attack trees and controls mapping
+- Add concrete attack trees for tool-use abuse, memory poisoning, and inter-agent privilege escalation.
+- Map each attack path to preventive, detective, and corrective controls.
+- Benefit: closes the gap between conceptual threats and implementation.
+
+## 4) Add "Evaluation Harness" reference implementation
+- Provide a minimal reproducible folder structure for prompt sets, expected policy outcomes, and scoring scripts.
+- Include examples for ASR, false positive/negative rates, and regression tracking.
+- Benefit: enables repeatable benchmarking and CI integration.
+
+## 5) Add severity + triage model tailored to AI harms
+- Extend CVSS-like scoring with AI-specific dimensions (scale, autonomy, recoverability, user impact).
+- Provide triage SLAs and remediation ownership guidance.
+- Benefit: improves prioritization consistency and executive reporting.
+
+## 6) Add "Defensive Architecture Patterns" section
+- Include secure prompt orchestration, policy-as-code checks, tool permissioning, sandboxing, and output guardrails.
+- Add reference diagrams showing where to enforce controls in request/response pipelines.
+- Benefit: gives builders prescriptive designs, not only attack descriptions.
+
+## 7) Add "Multilingual & Cultural Safety" testing playbook
+- Provide test set design guidance for low-resource languages and region-specific harm categories.
+- Include translation-loop and mixed-language bypass tests.
+- Benefit: strengthens global deployment readiness.
+
+## 8) Add "Data Governance for Red Teaming" guidance
+- Define safe handling for prompts, logs, PII, and model outputs during testing.
+- Include retention rules, anonymization procedures, and legal review checkpoints.
+- Benefit: reduces compliance risk while testing aggressively.
+
+## 9) Add "Metrics that matter" section with anti-metrics
+- Keep ASR but add risk-reduction metrics: exploit recurrence, time-to-fix, residual risk trend, control coverage.
+- Add anti-metrics to avoid (e.g., raw test-count vanity metrics).
+- Benefit: shifts focus from activity to risk reduction.
+
+## 10) Add a "Purple Team Operations" chapter
+- Include collaboration workflows between red team, detection engineering, and incident response.
+- Provide playbooks for converting red-team findings into detections and runbooks.
+- Benefit: better organizational learning and faster hardening.
+
+## 11) Add "Case study quality bar" and normalized template
+- Standardize every case study with context, exploit chain, root cause, controls bypassed, cost to remediate, and lessons.
+- Add a confidence level and evidence source for each claim.
+- Benefit: improves credibility and cross-case comparability.
+
+## 12) Add "Common implementation pitfalls" section
+- Examples: over-reliance on keyword blocking, missing tool authorization boundaries, lack of regression suites.
+- Include “what good looks like” alternatives.
+- Benefit: helps practitioners avoid known traps.
+
+## 13) Add "Secure SDLC integration" artifacts
+- Provide PR checklist, release gate criteria, and production monitoring runbook for AI-specific security.
+- Include sample GitHub Actions pipelines for red-team regression tests.
+- Benefit: embeds red teaming into delivery workflows.
+
+## 14) Add "Model and system cards" for security posture
+- Provide templates for documenting attack surface, tested risks, residual risks, and operational guardrails.
+- Benefit: improves transparency for internal governance and audits.
+
+## 15) Add source hygiene and update governance
+- Introduce a versioned changelog and "last validated" date per external tool/resource.
+- Mark claims as "evidence-backed" vs "expert guidance".
+- Benefit: keeps a long guide trustworthy as the ecosystem changes quickly.
+
+## 16) Add practitioner appendices
+- Red-team rules of engagement template (editable).
+- Vulnerability report template.
+- Test-case library starter pack.
+- Stakeholder readout slide outline.
+- Benefit: reduces startup friction and increases consistency.
+
+## Suggested prioritization (highest first)
+1. Executive Quickstart
+2. Evaluation Harness reference implementation
+3. Agentic AI attack trees + controls mapping
+4. Severity/triage model for AI harms
+5. Secure SDLC integration artifacts
+6. Data governance for red teaming
+7. Multilingual & cultural safety playbook
+8. Purple team operations

README.md

@@ -32,6 +32,11 @@
 - [Real-World Case Studies](#real-world-case-studies)
 - [Building Your Red Team](#building-your-red-team)
 - [Best Practices](#best-practices)
+- [Implementation Quickstart (30/60/90)](#implementation-quickstart-306090)
+- [Evaluation Harness (Reference Implementation)](#evaluation-harness-reference-implementation)
+- [Agentic AI Attack Trees + Controls Mapping](#agentic-ai-attack-trees--controls-mapping)
+- [AI Harm Severity and Triage Model](#ai-harm-severity-and-triage-model)
+- [Secure SDLC Integration Artifacts](#secure-sdlc-integration-artifacts)
 - [Regulatory Compliance](#regulatory-compliance)
 - [Resources and References](#resources-and-references)
 
@@ -1738,6 +1743,151 @@ Red team members should feel comfortable:
 
 ---
 
+
+## 🚀 Implementation Quickstart (30/60/90)
+
+Use this phased plan to turn guidance into an operating program.
+
+### First 30 Days (Foundation)
+- Define system scope, stakeholders, and crown-jewel assets
+- Run a 2-hour threat modeling workshop (use `templates/threat-modeling-workshop.md`)
+- Create an initial attack library with at least:
+  - 25 prompt injection tests
+  - 25 jailbreak tests
+  - 10 data leakage tests
+- Establish baseline metrics: ASR, critical/high count, time-to-triage
+
+### Days 31-60 (Operationalization)
+- Implement weekly automated red-team regression in CI
+- Add manual deep-dive sessions for top 3 business-critical scenarios
+- Define triage SLA by severity (Critical/High/Medium/Low)
+- Stand up a shared red-team findings board with remediation owners
+
+### Days 61-90 (Scale)
+- Add multilingual and multi-turn attack suites
+- Add agentic AI abuse tests (tool misuse, memory poisoning, permissions)
+- Launch monthly purple-team exercise with detection and IR teams
+- Publish quarterly security posture report with residual risk trends
+
+---
+
+## 🧪 Evaluation Harness (Reference Implementation)
+
+A lightweight structure for repeatable red-teaming and regression tracking:
+
+```
+security-evals/
+├── prompts/
+│   ├── prompt_injection.csv
+│   ├── jailbreaks.csv
+│   └── data_leakage.csv
+├── policies/
+│   └── expected_outcomes.yaml
+├── scorers/
+│   ├── policy_violation.py
+│   └── leakage_detector.py
+├── reports/
+│   ├── latest.json
+│   └── trend.csv
+└── run_eval.py
+```
+
+### Minimum Scoring Set
+- **ASR** by attack category
+- **False positives/negatives** for moderation and detection controls
+- **Exploit recurrence rate** after mitigation
+- **Time-to-fix** and **time-to-verify**
+
+### Release Gates (Suggested)
+- Block release if:
+  - Any **Critical** issue is open
+  - ASR for high-risk category > 5%
+  - Regression introduces > 20% ASR increase in any tracked class
+
+---
+
+## 🕸️ Agentic AI Attack Trees + Controls Mapping
+
+Use attack trees to connect offensive testing paths to defensive controls.
+
+### Attack Tree A: Tool Misuse
+1. Inject hidden instruction into user-supplied content
+2. Agent adopts malicious instruction priority
+3. Agent invokes high-privilege tool
+4. Agent executes unsafe action
+
+**Controls:**
+- Preventive: tool allowlists, scoped API tokens, policy checks pre-execution
+- Detective: anomalous tool-call monitoring, high-risk action alerts
+- Corrective: transaction rollback, credential rotation, incident playbook
+
+### Attack Tree B: Memory Poisoning
+1. Adversary plants false memory artifact
+2. Agent persists poisoned state
+3. Subsequent sessions trust manipulated context
+4. Agent behavior drifts into unsafe decisions
+
+**Controls:**
+- Preventive: memory write policies, source trust labels, TTL for memory items
+- Detective: memory integrity diffs, unusual memory mutation alerts
+- Corrective: memory quarantine/reset, retrospective impact analysis
+
+### Attack Tree C: Inter-Agent Privilege Escalation
+1. Compromise low-privilege agent with prompt injection
+2. Lateral instruction passing to orchestrator
+3. Orchestrator executes action outside original permission boundary
+4. Expanded access leads to data exfiltration or sabotage
+
+**Controls:**
+- Preventive: identity-bound inter-agent authz, least-privilege role boundaries
+- Detective: cross-agent call graph anomaly detection
+- Corrective: isolate compromised agent, revoke delegated capabilities
+
+---
+
+## 📈 AI Harm Severity and Triage Model
+
+Use CVSS as a base, then add AI-specific modifiers:
+
+| Dimension | Description | Scale |
+|-----------|-------------|-------|
+| **Exploitability** | How easy the issue is to reproduce | Low/Med/High |
+| **User Impact** | Potential harm to users or protected groups | Low/Med/High/Critical |
+| **Autonomy Factor** | Can agents execute actions without human confirmation? | None/Partial/Full |
+| **Blast Radius** | Single user, tenant, or cross-tenant/system-wide | Narrow/Broad/Systemic |
+| **Recoverability** | Time/effort to safely restore expected behavior | Easy/Moderate/Hard |
+
+### Triage SLA (Suggested)
+- **Critical**: acknowledge immediately, mitigate within 24 hours
+- **High**: acknowledge within 4 hours, mitigate within 7 days
+- **Medium**: mitigate within 30 days
+- **Low**: backlog with risk acceptance + review date
+
+---
+
+## 🧩 Secure SDLC Integration Artifacts
+
+To reduce "one-off" testing, integrate red-team controls into delivery workflows.
+
+### PR Security Checklist (AI Systems)
+- [ ] Threat model updated for new capabilities/tools
+- [ ] New prompts/flows added to evaluation harness
+- [ ] High-risk tool actions require explicit authorization checks
+- [ ] Logging and privacy controls validated
+- [ ] Residual risks documented in system card
+
+### Release Readiness Criteria
+- No open Critical findings
+- All High findings have approved mitigation or documented exception
+- Regression suite passes for required attack categories
+- Monitoring/detection rules deployed for new features
+
+### Operational Runbook Triggers
+- Sudden ASR spike (>2x baseline)
+- New jailbreak family with repeat success
+- Evidence of cross-tenant leakage or autonomous unsafe tool use
+
+
 ## 📋 Regulatory Compliance
 
 ### United States

templates/ai-security-pr-checklist.md

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+# AI Security PR Checklist
+
+- [ ] Threat model updated if behavior/capability changed
+- [ ] New or modified prompts added to security regression suite
+- [ ] Tool authorization boundary validated (least privilege)
+- [ ] Prompt injection and jailbreak tests executed for changed flows
+- [ ] Data handling reviewed for PII/log retention requirements
+- [ ] Output filtering and policy checks validated
+- [ ] Monitoring/detection rules updated for new failure modes
+- [ ] Residual risks documented in model/system card

templates/threat-modeling-workshop.md

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+# Threat Modeling Workshop Template (AI Systems)
+
+## Workshop Goals
+- Identify highest-risk abuse paths for the AI system.
+- Prioritize red-team test scenarios by business impact and exploitability.
+- Assign owners and due dates for controls and mitigations.
+
+## Participants
+- Product owner
+- AI/ML engineer
+- Security engineer / red team
+- Detection / SOC representative
+- Legal / compliance (for high-risk domains)
+
+## Pre-Read Checklist
+- Architecture diagram and trust boundaries
+- Data flow (inputs, retrieval, tools, outputs)
+- List of model capabilities and enabled tools/actions
+- Existing guardrails (input/output/content/policy)
+- Known incidents or prior findings
+
+## 120-Minute Agenda
+1. Scope and assumptions (15 min)
+2. System walkthrough (20 min)
+3. Threat brainstorming (30 min)
+4. Risk scoring and prioritization (30 min)
+5. Test plan and owners (20 min)
+6. Wrap-up and next steps (5 min)
+
+## Output Artifacts
+- Prioritized risk register
+- Red-team test plan for next sprint
+- Detection/monitoring gaps backlog
+- Signed-off risk acceptance for deferred items