Skill Guide

Cybersecurity threat intelligence frameworks (MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF)

A set of structured knowledge bases and risk management frameworks (MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF) that catalog, classify, and mitigate adversarial tactics, techniques, and procedures (TTPs) targeting machine learning systems, large language models, and broader AI implementations.

Organizations require these frameworks to proactively defend AI/ML assets against evolving threats, ensuring operational resilience and regulatory compliance. Proficiency translates directly into reduced breach risk, secured intellectual property, and protected brand reputation.

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How to Learn Cybersecurity threat intelligence frameworks (MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF)

Start with the core documentation: MITRE ATLAS matrix structure, OWASP LLM Top 10 risk categories, and NIST AI RMF governance functions (Map, Measure, Manage, Govern). Focus on understanding threat taxonomies and the AI system lifecycle from a risk perspective.

Apply frameworks to analyze real-world AI system architectures. Map specific vulnerabilities (e.g., prompt injection, data poisoning) to mitigations in ATLAS and OWASP controls. Avoid treating frameworks as static checklists; integrate them into threat modeling sessions.

Synthesize frameworks into enterprise-wide AI security policies and incident response playbooks. Architect defense-in-depth strategies for high-value ML pipelines. Mentor teams on proactive threat hunting using ATLAS TTPs and conduct red team exercises based on OWASP scenarios.

Practice Projects

Beginner

Project

Threat Mapping for a Simple Chatbot

Scenario

A customer-facing LLM chatbot is deployed. Your task is to identify potential threats using the OWASP LLM Top 10 and map them to the MITRE ATLAS framework.

How to Execute

1. Identify the chatbot's components (API, model endpoint, training data source). 2. Cross-reference each OWASP LLM Top 10 risk (e.g., LLM01: Prompt Injection) with the chatbot's architecture. 3. For each identified risk, find the corresponding adversarial tactic in ATLAS (e.g., TA0001: Initial Access). 4. Document a basic mitigation strategy from the framework for the top 3 risks.

Intermediate

Case Study/Exercise

Conducting a Risk Assessment for an Internal ML Pipeline

Scenario

Your organization is deploying a predictive maintenance model on IoT sensor data. You must lead a risk assessment workshop using NIST AI RMF and OWASP guidelines.

How to Execute

1. Map the ML pipeline stages (data ingestion, model training, deployment, monitoring) to NIST AI RMF functions. 2. Facilitate a brainstorming session to identify threats at each stage (e.g., data poisoning during ingestion, model evasion in deployment). 3. Use OWASP LLM Top 10 and ATLAS to categorize each threat. 4. Prioritize risks based on likelihood and impact, then draft a mitigation plan referencing specific controls.

Advanced

Project

Designing an AI Security Governance Program

Scenario

As the Head of AI Security, you must establish a governance program that integrates MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF to protect all company AI assets.

How to Execute

1. Define policies that mandate threat modeling using ATLAS and OWASP for all AI projects. 2. Create a continuous monitoring program aligned with NIST AI RMF's 'Measure' and 'Manage' functions. 3. Develop an incident response playbook with specific procedures for AI-specific attacks (e.g., adversarial example attacks, model inversion). 4. Establish a cross-functional review board to evaluate new AI deployments against the integrated framework.

Tools & Frameworks

Threat Intelligence & Knowledge Bases

MITRE ATLAS MatrixOWASP LLM Top 10 ProjectNIST AI Risk Management Framework (AI RMF)

Apply these as primary reference architectures for threat identification, risk classification, and mitigation strategy development during system design, assessment, and incident response.

Technical & Operational Tools

Microsoft CounterfitGarak LLM Vulnerability ScannerAI Security Incident Response Team (AISIRT) Playbooks

Use specialized tools to execute red team assessments, probe LLMs for vulnerabilities, and implement operational security procedures derived from the frameworks.

Interview Questions

Answer Strategy

Structure the answer by walking through the ATLAS matrix tactics in sequence. Sample Answer: 'I would start with Reconnaissance (TA0000) to map the model's exposure surface-its API endpoints, training data source, and documentation. Then, under Resource Development (TA0002), I'd assess if an adversary could acquire similar data. For Initial Access (TA0001), I'd evaluate the attack surface of the serving infrastructure. The core analysis would focus on ML Model Access (TA0000) to determine if the model can be queried for inversion or extraction, and finally, Impact (TA0002) to classify the business consequence of model compromise.'

Answer Strategy

This tests the ability to operationalize high-level frameworks. Sample Answer: 'I translate 'Govern' by establishing specific, measurable policies: mandating that every AI project begins with a threat model document that references OWASP LLM Top 10; requiring quarterly risk reviews aligned with the ATLAS matrix; and instituting a 'AI Security Champion' role in each dev team whose responsibility includes updating the risk register based on new threat intelligence.'