Threat modeling using STRIDE/PASTA for AI
Threat modeling using STRIDE/PASTA for AI involves systematically identifying and assessing potential security threats specific to artificial intelligence systems. STRIDE focuses on six threat categories: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. PASTA (Process for Attack Simulation and Threat Analysis) offers a risk-centric approach, aligning business objectives with technical threats. Applying these frameworks helps uncover vulnerabilities unique to AI, such as data poisoning or model inversion, and guides mitigation strategies.
Threat modeling using STRIDE/PASTA for AI
Threat modeling using STRIDE/PASTA for AI involves systematically identifying and assessing potential security threats specific to artificial intelligence systems. STRIDE focuses on six threat categories: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. PASTA (Process for Attack Simulation and Threat Analysis) offers a risk-centric approach, aligning business objectives with technical threats. Applying these frameworks helps uncover vulnerabilities unique to AI, such as data poisoning or model inversion, and guides mitigation strategies.
💡 Key Takeaways
- Understand how threat modeling fits into AI risk assessment and how STRIDE and PASTA guide the process.
- Identify AI threats across STRIDE categories (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) and how they appear in data, models, and APIs.
- Apply STRIDE/PASTA to AI lifecycle stages (data collection, training, deployment, inference) to systematically uncover vulnerabilities.
- Translate identified threats into practical mitigations and risk prioritization for AI systems.
❓ Frequently Asked Questions
What is STRIDE in threat modeling, and what are its six threat categories?
STRIDE is a mnemonic for six security threat categories: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. In AI, apply these to models, data pipelines, APIs, and infrastructure to identify potential threats.
What is PASTA in threat modeling, and how is it used for AI?
PASTA stands for Process for Attack Simulation and Threat Analysis. It is a risk-focused threat modeling methodology with seven stages that help assess attacker goals and business impact, guiding AI-specific threat analysis from design to deployment.
How do STRIDE and PASTA complement each other in AI risk assessment?
PASTA provides the workflow and risk emphasis, while STRIDE supplies the threat categories to classify threats found during the process. Together, they help identify AI threats systematically and prioritize mitigations by likelihood and impact.
Can you provide quick examples of STRIDE categories in AI contexts?
Examples in AI: Spoofing—impersonating a user to access AI services; Tampering—altering training data or model parameters; Repudiation—insufficient logs to prove actions; Information Disclosure—leakage of training data or prompts; Denial of Service—overloading inference endpoints; Elevation of Privilege—gaining higher rights to modify models or data.