AI Model Risk Management & Red Teaming
Assess and secure your AI/ML models with specialized risk management and red teaming.
Why AI Model Risk Management Matters
AI and machine learning systems introduce entirely new categories of risk that traditional security and compliance programs were not designed to address — from adversarial attacks and prompt injection to training data poisoning, model theft, and algorithmic bias. As AI adoption accelerates and regulations like the EU AI Act and NIST AI RMF mature, organizations need structured risk management that covers the full AI lifecycle.
Our AI model risk management service provides comprehensive assessment, governance, and red teaming for AI and ML systems deployed in production environments. We evaluate your models against established frameworks including the NIST AI Risk Management Framework, OWASP Top 10 for Large Language Model Applications, and industry-specific guidelines for healthcare, financial services, and regulated industries.
Our AI red teaming goes beyond traditional security testing to evaluate the unique attack surfaces of modern AI systems — including LLM-powered applications, agentic AI systems, computer vision models, and traditional ML pipelines. We simulate real-world adversarial attacks, identify exploitable vulnerabilities, and provide actionable remediation guidance that helps you deploy AI with confidence.
Why Choose Our Compliance Services
Experience the advantages of working with certified compliance experts who understand your business needs
Framework-Aligned Assessment
We evaluate your AI systems against the NIST AI Risk Management Framework, OWASP Top 10 for LLMs, and EU AI Act requirements — producing a prioritized risk register that maps findings to specific compliance obligations and business impact scenarios.
Adversarial Red Teaming
Our security engineers simulate real-world adversarial attacks — prompt injection, jailbreaking, training data extraction, model evasion, and agentic AI misuse scenarios — using the same techniques that sophisticated threat actors employ against production AI systems.
Actionable Remediation Roadmap
Every assessment delivers specific, implementable remediation guidance — not just a list of findings. We work alongside your ML and security teams to prioritize fixes by exploitability and business impact, then verify remediation through targeted retesting before you go to production.
Our AI Model Risk Process
From assessment to red teaming.
Model Inventory & Assessment
We begin by cataloging every AI and ML system in your environment — including models embedded in third-party products that your team may not be tracking. For each system, we assess its risk profile, data inputs, decision authority, deployment architecture, and current governance status to establish a comprehensive baseline.
Risk Analysis & Vulnerability Mapping
Our security engineers evaluate each model against the NIST AI Risk Management Framework and OWASP Top 10 for LLMs. We identify specific attack surfaces including prompt injection vectors, training data poisoning risks, model extraction vulnerabilities, and adversarial input weaknesses — then classify each finding by severity, exploitability, and business impact.
Red Teaming & Remediation
We simulate real-world adversarial attacks against your AI systems using the same techniques that threat actors employ. Our red team tests LLM applications for prompt injection, jailbreaking, and sensitive data extraction; evaluates traditional ML models for evasion and poisoning attacks; and provides detailed remediation guidance with prioritized action items and implementation support.
Model Inventory & Assessment
We begin by cataloging every AI and ML system in your environment — including models embedded in third-party products that your team may not be tracking. For each system, we assess its risk profile, data inputs, decision authority, deployment architecture, and current governance status to establish a comprehensive baseline.
Risk Analysis & Vulnerability Mapping
Our security engineers evaluate each model against the NIST AI Risk Management Framework and OWASP Top 10 for LLMs. We identify specific attack surfaces including prompt injection vectors, training data poisoning risks, model extraction vulnerabilities, and adversarial input weaknesses — then classify each finding by severity, exploitability, and business impact.
Red Teaming & Remediation
We simulate real-world adversarial attacks against your AI systems using the same techniques that threat actors employ. Our red team tests LLM applications for prompt injection, jailbreaking, and sensitive data extraction; evaluates traditional ML models for evasion and poisoning attacks; and provides detailed remediation guidance with prioritized action items and implementation support.
Unassessed vs. Assessed Models
Why AI model risk management is critical.
| Feature | Unassessed | Assessed |
|---|---|---|
| Risk Awareness | Low | High |
| Mitigation | Unclear | Actionable |
Risk Awareness
Mitigation
AI 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.
Assess Your AI Models
Book a free AI risk assessment with our security engineers.