7-Layer AI Governance Architecture
Our framework implements defense-in-depth across seven interdependent control layers, from executive governance committees to continuous compliance monitoring. Every layer is designed for organizations operating under the strictest regulatory environments.
AI Governance Committee
Executive-level oversight body (CISO, CTO, CDO, CRO, CLO) with unanimous vote authority over all AI model adoption, operation, and decommissioning decisions.
Cybersecurity Defense-in-Depth
Seven nested security perimeters: WAF/mTLS 1.3, network segmentation, AI Gateway with prompt sanitization, AES-256/BYOK encryption, Zero Trust IAM, SIEM/SOAR, and dedicated AI-CERT incident response.
Data Governance & Classification
5-tier data taxonomy (Public → Classified) with strict whitelisting: everything not explicitly permitted is prohibited. Multi-layer DLP with PII scanning, automated classification, and token budgets by clearance level.
API Key & Credential Management
Centralized secrets vault with HSM backing, automated 90-day rotation, sub-60-second revocation, and strict RBAC. Keys are never stored in code, configs, chats, or repositories — injected at runtime via secure sidecar.
Performance & SLA Enforcement
10+ KPIs with hard thresholds: P95 latency <5s, uptime ≥99.95%, TTFT <500ms. Intelligent semantic caching, dynamic model routing, rate limiting per role, and contractual SLAs with escalating financial penalties.
Compliance & Continuous Audit
Full regulatory coverage: ISO 27001/42001, NIST AI RMF, SOC 2 Type II, PCI-DSS v4, BCBS 239, DORA, EU AI Act, NIST 800-171, GDPR, ITAR. Immutable WORM logs retained 7-10 years with Policy-as-Code enforcement.
AI Model Approval Pipeline
Every AI model entering the organization must pass through a formalized lifecycle with mandatory approval gates — from business justification to decommissioning.
01 — Business Case
Formal justification with ROI analysis, alternative assessment (including non-AI), and executive sponsorship.
02 — Provider Selection & Due Diligence
Weighted evaluation across 13 criteria. 8 non-negotiable eliminatory requirements including ZDR, SOC 2 Type II, and contractual data non-training guarantees.
03 — Sandbox PoC
Isolated execution with synthetic or anonymized data only. Maximum 90-day duration with quality benchmarks.
04 — Security Assessment
Penetration testing, architecture review, data flow analysis, OWASP LLM Top 10 threat modeling, and residual risk evaluation.
05 — Committee Gate
Mandatory unanimous approval from all voting members of the AI Governance Committee.
06 — Controlled Deployment
Canary rollout with feature flags and intensive monitoring during the first 30 days. API keys provisioned via Vault.
07 — Continuous Monitoring
Real-time dashboards tracking latency, throughput, error rates, cost per token, and AI-specific threat detection.
08 — Periodic Review
Semi-annual re-evaluation against current criteria. Renewal or sunset decision with full stakeholder sign-off.
09 — Decommissioning
Formal retirement with data migration, certified credential destruction, access revocation, and compliance closure report.
AI Model Penetration Testing
Our specialized AI pentesting methodology goes beyond traditional application security. We simulate adversarial attacks targeting the unique threat surface of LLM-based systems — from prompt injection to data exfiltration through model manipulation.
Reconnaissance & Threat Modeling
Map the AI attack surface: API endpoints, model configurations, data flows, authentication mechanisms, system prompts, and integration points. Build threat model using OWASP Top 10 for LLMs.
Prompt Injection Testing
Systematic testing of direct and indirect prompt injection vectors: delimiter escapes, meta-prompt manipulation, role-play attacks, context window poisoning, instruction override, and jailbreak pattern libraries.
Data Exfiltration Attempts
Verify whether sensitive data can be extracted through carefully crafted prompts: system prompt disclosure, training data extraction, PII leakage through context manipulation, and cross-session information bleeding.
Model Abuse & Misuse Testing
Test guardrail effectiveness: generating prohibited content, bypassing safety filters, causing harmful outputs, resource exhaustion attacks (token flooding), and model behavior manipulation through adversarial inputs.
API & Infrastructure Security
Traditional pentest augmented for AI: authentication bypass, rate limit evasion, API key exposure testing, credential rotation verification, TLS configuration, and secrets management audit.
DLP & Privacy Validation
End-to-end validation of data loss prevention controls: PII scanner effectiveness, classification engine accuracy, output filtering robustness, consent verification, and right-to-erasure compliance.
Engagement Deliverables
Executive Summary Report
C-level risk overview with severity classification, business impact assessment, and strategic remediation roadmap.
Technical Findings Document
Detailed vulnerability report with reproduction steps, evidence, CVSS scoring, and specific remediation instructions for each finding.
AI Governance Framework Blueprint
Complete governance documentation: committee structure, data classification policies, API key management procedures, and lifecycle controls.
Compliance Mapping Matrix
Gap analysis against ISO 27001, NIST AI RMF, SOC 2, PCI-DSS, EU AI Act, GDPR, and sector-specific regulations with remediation priorities.
Pentest Evidence Package
Full audit trail with timestamped logs, request/response captures, prompt injection payloads used, and remediation verification results.
Incident Response Playbooks
Ready-to-deploy runbooks for AI-specific incidents: prompt injection containment, API key compromise protocol, data exfiltration response, and Shadow AI detection.
Monitoring & KPI Dashboard Spec
Technical specification for real-time AI operations monitoring: latency percentiles, error rates, token costs, security alerts, and compliance status indicators.
Standards & Certifications
Every engagement is mapped to applicable international standards: