AI Model Risk Management & Red Teaming
NIST AI RMF-aligned risk assessment plus LLM red-teaming with Garak, PyRIT, and Promptfoo. OWASP Top 10 for LLMs 2025 coverage end-to-end.
Why AI Model Risk Management Matters
AI risk management is now a regulatory expectation, not an emerging concern. The NIST AI Risk Management Framework (RMF 1.0, January 2023) defines four core functions -- Govern, Map, Measure, Manage -- that buyers, regulators, and increasingly procurement processes ask vendors to align with. The EU AI Act became effective in August 2024 with phased compliance through 2026, classifying AI systems into prohibited, high-risk, limited-risk, and minimal-risk categories with corresponding obligations. Healthcare AI also intersects with HIPAA and HITRUST AI requirements.
LLM-specific risks fit poorly into traditional security frameworks. The OWASP Top 10 for Large Language Model Applications (2025 edition) catalogs the new attack surface: LLM01 prompt injection, LLM02 sensitive information disclosure, LLM03 supply chain (model and data poisoning), LLM04 data and model poisoning, LLM05 improper output handling, LLM06 excessive agency, LLM07 system prompt leakage, LLM08 vector and embedding weaknesses, LLM09 misinformation, and LLM10 unbounded consumption. Each requires testing, controls, and runbook treatment distinct from web app security.
Our team brings AI expertise as a core differentiator: red-team engagements run with Garak (open-source LLM probing), Microsoft PyRIT (Python Risk Identification Toolkit), Promptfoo for prompt-level evaluation, and custom harnesses against your model. Defenses we help implement include output filtering (Lakera Guard, NVIDIA NeMo Guardrails), prompt-injection detection, RAG-poisoning defenses, and rate-limiting tied to abuse detection. Because our team's roots are in audit and compliance work, we pair the technical work with documentation that satisfies NIST AI RMF, EU AI Act conformity assessments, and customer AI governance questionnaires.
Why Choose Our AI Model Risk Management & Red Teaming
Engineering rigor, audit-ready process, and operational depth across cloud, SaaS, and software delivery
Specialized Assessment
NIST AI RMF Govern/Map/Measure/Manage applied end-to-end. Model inventory, threat modeling per OWASP LLM Top 10, and AI system documentation that satisfies EU AI Act and customer governance asks.
Red Teaming
Automated probing with Garak and PyRIT, manual prompt-injection and jailbreak testing, RAG poisoning attempts, and model extraction probes. Findings delivered with reproduction prompts and severity scoring.
Mitigation Guidance
Defense implementation: output filters (Lakera Guard, NeMo Guardrails), prompt-injection detection, RAG content provenance, and abuse-detection-tied rate limiting -- pragmatic stack choices, not just recommendations.
Our AI Model Risk Process
From assessment to red teaming.
Model Inventory & NIST RMF Mapping
Two-to-four weeks: catalog every AI/ML model in production (foundation models, fine-tunes, embedding models, RAG pipelines), map each to NIST AI RMF functions, and document use cases for EU AI Act risk classification. Output: AI system inventory and risk register.
Red-Team Engagement
Three to six weeks per model: automated probing with Garak and PyRIT, manual exploitation against OWASP LLM Top 10 categories, RAG poisoning and prompt injection scenarios, and model extraction tests where authorized. Daily sync to triage critical findings.
Mitigation & Continuous Monitoring
Implement defenses (output filtering, prompt injection detection, RAG provenance, abuse-detection rate limits), document control evidence per NIST AI RMF, and stand up continuous evaluation harness so model behavior changes get flagged in CI before production.
Model Inventory & NIST RMF Mapping
Two-to-four weeks: catalog every AI/ML model in production (foundation models, fine-tunes, embedding models, RAG pipelines), map each to NIST AI RMF functions, and document use cases for EU AI Act risk classification. Output: AI system inventory and risk register.
Red-Team Engagement
Three to six weeks per model: automated probing with Garak and PyRIT, manual exploitation against OWASP LLM Top 10 categories, RAG poisoning and prompt injection scenarios, and model extraction tests where authorized. Daily sync to triage critical findings.
Mitigation & Continuous Monitoring
Implement defenses (output filtering, prompt injection detection, RAG provenance, abuse-detection rate limits), document control evidence per NIST AI RMF, and stand up continuous evaluation harness so model behavior changes get flagged in CI before production.
Unassessed vs. Assessed Models
Why AI model risk management is critical.
| Feature | Unassessed | Assessed |
|---|---|---|
| Risk Framework Alignment | Ad-hoc internal review, no documented framework | NIST AI RMF and EU AI Act-aligned with documented evidence |
| Red-Team Methodology | Manual prompts, no systematic probe library | Garak, PyRIT, and Promptfoo with OWASP LLM Top 10 coverage |
Risk Framework Alignment
Red-Team Methodology
Agentic AI Governance Implementation Guide
Policy template, agent inventory, and 12-month roadmap for governing the agents your employees are already running.
Read the whitepaperAI Risk Management — Delivered by TrustEdge.ai
For AI model risk management and red teaming, we direct clients to TrustEdge.ai — our dedicated AI services division — where specialized expertise in AI governance, model security assessment, and production MLOps is the core focus. TrustEdge is built on the same compliance rigor that Jacobian clients have relied on for over 15 years.
Learn More at TrustEdge.aiAI Model Risk FAQs
Common questions about AI model risk management and red teaming.
Related Services
Buyers of ai model risk management & red teaming typically partner with us across these adjacent disciplines
Penetration Testing
AI red-teaming uses different tools than traditional pen-testing — Garak, PyRIT, Promptfoo, Lakera Guard. We run the AI track alongside the application track for full coverage.
Compliance Program Management
NIST AI RMF and ISO/IEC 42001 are the practical governance frameworks. Compliance program management covers the recurring cadence — risk reviews, model registry, incident response.
SOC 2 Compliance
Enterprise buyers increasingly ask vendors with AI features for AI-specific control sections in SOC 2 reports. We design the controls so they evidence cleanly.
Assess Your AI Models
Book a free AI risk assessment with our security engineers.