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Evaluating Security Risks of Generative AI Systems Today

Evaluating Security Risks of Generative AI Systems Today

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Evaluating Security Risks of Generative AI Systems Today

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  1. Evaluating Security Risks of Generative AI Systems Today Understanding OWASP and NIST-aligned risk evaluation for GenAI systems

  2. Why Generative AI Security Risk Evaluation Matters • Generative AI systems introduce unique security risks affecting data, operations, and decision quality. Organizations increasingly align risk evaluation with the OWASP Top 10 for LLM Applications and the NIST Generative AI Profile. Managers play a critical role in defining access, governance, and approval boundaries, which is why a generative AI course for managers is often linked to risk governance.

  3. Common Attack Paths in GenAI Applications • OWASP identifies prompt injection as a major risk in LLM applications, enabling unauthorised access, information leakage, and altered decision-making. Attackers target chat interfaces, document summarizers, and agent-style workflows. Indirect prompt injection occurs when external content such as files or web pages embeds hidden instructions that influence model behavior.

  4. Insecure Output Handling and Operational Risk • OWASP classifies insecure output handling as a critical risk when model outputs are treated as trusted commands or executable code. This can lead to downstream exploits such as code execution and system compromise. Managers mitigate this risk through workflow design, tool permission limits, and human approval for high-risk actions.

  5. Data Leakage and Privacy Exposure • Sensitive information disclosure includes exposure of PII, financial data, health records, credentials, and confidential business data. OWASP and NIST both highlight weak data protection and data memorization as sources of privacy risk. Models may infer sensitive information by combining multiple data sources, even if it is not present in the prompt.

  6. Privacy Attacks and Model Abuse • NIST AI 100-2e2023 identifies privacy attacks such as membership inference, data reconstruction, and model extraction. These threats arise through repeated query access to model interfaces. Managers often decide where public access ends and internal access begins, making governance training essential in a Gen AI course for managers.

  7. Model, Supply Chain, and Lifecycle Threats • OWASP highlights supply chain vulnerabilities involving third-party models, datasets, plugins, and fine-tuning adapters. Training data poisoning and model poisoning attacks can introduce unsafe or manipulated behaviors. NIST AI 600-1 emphasizes that GenAI expands the attack surface and must be treated as a security architecture decision.

  8. Manager-Led Controls and Governance Alignment • NIST AI 600-1 positions the Generative AI Profile alongside the NIST AI Risk Management Framework for governance and risk control. Key controls include least-privilege access, pre-deployment testing, incident disclosure, and content provenance. A Best Generative AI course for managers aligns these controls with practical approval flows and escalation paths.

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