CoSAI Risk Map: In-depth Analysis of the AI System Security Risk Assessment Framework

This article provides an in-depth analysis of the open-source CoSAI Risk Map framework promoted by OASIS Open. Addressing the unique challenges of AI system security risks (limitations of traditional software security practices), it offers a systematic methodology for risk identification, analysis, and mitigation, helping organizations address security risks at all stages of the AI development lifecycle and uphold the bottom line of innovation.

With the rapid development of artificial intelligence technology, AI systems have permeated various industries. However, traditional software security practices struggle to effectively address AI's complexity, opacity, and data dependency. Against this backdrop, the CoSAI Risk Map framework emerged, providing a systematic methodology for identifying, analyzing, and mitigating AI system security risks.

CoSAI Risk Map is an open-source project promoted by OASIS Open, aiming to establish a common language and shared understanding of AI system security risks. Its core goal is to help organizations identify and address security risks at all stages of the AI development lifecycle. Unique risks faced by AI systems include: data poisoning attacks, model theft, adversarial examples, and supply chain risks.

The CoSAI framework emphasizes the importance of risk identification, with risks coming from multiple dimensions:

• Data layer: Biases in training data, labeling errors, malicious injection, etc. It is recommended to establish strict data governance processes (source verification, quality inspection, audit trails);
• Model layer: Architecture selection, training configuration, post-deployment behavior monitoring (the unpredictability of generative AI outputs increases complexity);
• Infrastructure layer: Security vulnerabilities in GPU clusters, containerized platforms, and cloud services threaten system integrity.

After identifying risks, in-depth analysis is required. The framework provides a structured method to understand each risk in terms of: occurrence probability (based on historical data and threat intelligence), impact scope (affected systems and business processes), mitigation difficulty (resource and technical complexity), and detection capability (detection ability of existing monitoring mechanisms). A risk priority matrix is established through multi-dimensional analysis to focus resources on addressing key risks.

The framework proposes a layered defense strategy:

• Preventive controls: Input validation and cleaning, model robustness training (adversarial training), access control authentication, data encryption and privacy protection;
• Detective controls: Model behavior monitoring and drift detection, query pattern analysis and alerting, output security filtering, audit log tracing;
• Responsive controls: Model rollback and version switching, incident response process division, external community collaboration, post-incident review and experience accumulation.

The framework is highly operable, and organizations can implement it step by step: assess the current situation (use a checklist to evaluate AI security maturity), develop a roadmap (phased improvement plan), establish governance mechanisms (clarify responsible persons and cross-departmental collaboration), and conduct continuous monitoring (integrate into daily operations to form a closed loop). Regarding industry impact, the improvement of AI regulatory laws (such as the EU AI Act) makes risk assessment a mandatory compliance requirement; in the future, the framework will be updated to cover emerging risks (multimodal models, autonomous behavior of AI Agents, etc.), so it is necessary to participate in open-source communities to track developments.

AI security is a systems engineering that needs to be considered from the initial design stage. The CoSAI framework provides a structured starting point, helping organizations uphold the security bottom line while pursuing AI innovation. In today's era of rapid AI technology iteration, establishing systematic risk management capabilities will become an important part of an enterprise's core competitiveness.