AI Risk Management

AI risk management encompasses the processes and frameworks organizations use to identify, assess, and mitigate potential harms from artificial intelligence systems across their operations. This includes evaluating risks related to model accuracy, bias, security vulnerabilities, regulatory compliance, and unintended consequences that could affect customers, employees, or stakeholders. For enterprises, effective AI risk management is essential because it reduces liability exposure, builds stakeholder trust, ensures regulatory compliance, and enables responsible deployment of AI at scale.

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ResearchGlobal2026-09-09

GenAI Pen-Testing Guide Exposes Structural Gaps in Enterprise Red-Teaming Programs

CSO Online published a structured practitioner guide for penetration testing LLM, RAG, and GenAI applications, framing prompt injection as a campaign-level risk rather than a single-endpoint problem. The guide maps attack surfaces across retrieval pipelines, vector databases, tool integrations, and downstream APIs. It references the OWASP LLM guidance and the NIST 2025 adversarial machine-learning taxonomy as applicable compliance frameworks.

prompt injection LLM security RAG red teaming adversarial testing AI risk management OWASP