Defending Against Adversarial AI: Implementing NIST, OWASP, and MITRE ATLAS Frameworks

Organizations face escalating threats from adversarial AI, specifically via prompt injection, data poisoning, and model inversion. Defending these assets requires a layered integration of the NIST AI Risk Management Framework for governance, the OWASP LLM Top 10 for application-level mitigation, and the MITRE ATLAS framework for tactical TTP mapping. Recent empirical research indicates a significant divergence between expert-perceived risks and actual incident frequency in CVE and GHSA datasets. To close this gap, security teams must implement a unified defense-in-depth strategy that synchronizes technical controls across the AI lifecycle—from data collection to inference—utilizing red-teaming playbooks and automated detection logic to mitigate model corruption and data exfiltration.

• Strategic Context: Governance and Risk Management

  • Implementation of the NIST AI Risk Management Framework (RMF) to establish organizational risk appetite and trustworthiness standards.
  • Integration of AI-specific governance into existing enterprise risk management (ERM) lifecycles.
  • Mapping high-level policy objectives to granular technical security requirements throughout the model lifecycle.

• Threat Model: Application-Layer Vulnerabilities

  • Mitigation of OWASP LLM Top 10 risks, focusing on prompt injection, insecure output handling, and training data poisoning.
  • Addressing the technical delta between theoretical vulnerabilities and real-world exploitation observed in the wild.
  • Deployment of developer-centric security controls during the fine-tuning and inference stages of model deployment.

• Tactical Intelligence: MITRE ATLAS Integration

  • Mapping adversary Tactics, Techniques, and Procedures (TTPs) to specific AI model components and data pipelines.
  • Utilization of MITRE ATLAS-based red-teaming playbooks to simulate evasion and model inversion attacks.
  • Development of advanced detection logic and signatures to identify anomalous behavioral patterns in AI workloads.

• Engineering: Unified Defense Implementation

  • Creation of a Unified Defense Mapping to cross-link NIST controls with OWASP vulnerabilities and ATLAS TTPs.
  • Enforcement of technical specifications for securing the full AI lifecycle, including data collection, training, and output.
  • Application of classifier performance benchmarks—measuring precision, recall, and balanced accuracy—to validate threat detection efficacy.

• Empirical Analysis: Data-Driven Defense

  • Addressing the statistical discrepancy between expert consensus and actual incident data using Cohen's $\kappa$ and Spearman $\rho$ analysis.
  • Leveraging incident corpora from CVE, GHSA, and OSV to prioritize defensive engineering efforts.
  • Modeling business impacts, including the potential for model corruption, data exfiltration, and long-term reputational damage.