In my previous posts, we’ve explored the imperative of building resilient AI architectures through adversarial robustness pipelines and the continuous testing that underpins them. I spoke about MITRE ATLAS and today, I want to introduce it as it is a crucial framework that provides a common language and structured approach to understanding and defending against sophisticated threats: MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems).

For those of us deeply entrenched in cybersecurity, the MITRE ATT&CK framework is likely second nature. It’s a globally accessible knowledge base of adversary tactics and techniques based on real-world observations. MITRE ATLAS extends this invaluable work specifically to the realm of Artificial Intelligence and Machine Learning (AI/ML), offering a comprehensive overview of how adversaries can attack and manipulate AI systems.

Just as ATT&CK helps us understand traditional cyber threats, ATLAS provides a structured way to categorise and understand the unique attack vectors targeting AI models and the data they rely on. This is incredibly powerful for security architects and teams looking to proactively secure their AI investments.

Why MITRE ATLAS Matters for Security Architects

As a security architect, my role isn’t just to react to incidents, but to design systems that are inherently secure. MITRE ATLAS gives us a strategic lens to do precisely that for AI:

1. Common Language for AI Threats: Before ATLAS, discussions around AI security could often be abstract. ATLAS provides a standardised taxonomy for AI-specific adversarial behaviours, allowing security, AI engineering, and business teams to communicate about threats with greater clarity and precision.

2. Structured Threat Modelling: It enables a more systematic approach to threat modelling for AI/ML systems. Instead of guessing potential attack points, we can walk through the ATLAS framework, identifying relevant tactics and techniques that might apply to our specific AI applications. This leads to more comprehensive and proactive defence strategies.

3. Informed Defence Strategies: By understanding the specific methods adversaries use – from data poisoning to model inversion attacks – we can design more targeted and effective controls. ATLAS helps us answer questions like: “If an adversary is trying to manipulate our training data (a ‘Tactic: Evasion’ within ATLAS), what specific ‘Techniques’ might they use, and what ‘Mitigations’ can we put in place?”

4. Assessing Tooling and Capabilities: When evaluating security tools for AI/ML (as we discussed in the Adversarial Robustness Pipelines post), ATLAS provides a benchmark. Does a tool cover a wide range of ATLAS techniques? Does it help detect or mitigate specific tactics outlined in the framework?

5. Incident Response Planning: In the unfortunate event of an AI-specific security incident, ATLAS can aid in post-incident analysis, helping to identify the specific adversary techniques used and inform future preventative measures.

Exploring Key Concepts

MITRE ATLAS organises adversarial behaviour into Tactics (the ‘why’ or objective of an attack, e.g., ‘Model Evasion’, ‘Data Poisoning’) and Techniques (the ‘how’ or specific methods used, e.g., ‘Adversarial Examples’, ‘Backdoors’). It also maps Common Knowledge – the shared understanding or information an adversary might use – and importantly, Mitigations – the actions or controls that can reduce the impact or likelihood of a technique.

For example, if we consider the Tactic of “Model Evasion,” an adversary’s goal might be to cause a deployed AI model to misclassify an input. Common Techniques might include:

• Adversarial Examples: (e.g., adding imperceptible noise to an image to fool a facial recognition system).

• Feature Perturbation: (e.g., subtly altering specific data points to change a model’s prediction).

For these techniques, ATLAS suggests Mitigations such as:

• Adversarial Training: (training the model on adversarial examples to improve its robustness).
• Input Validation/Sanitisation: (checking inputs for suspicious patterns before they reach the model).
• Defensive Distillation: (creating a more robust model from an existing one).

Integrating ATLAS into Your Security Posture

So, how can security architects practically integrate MITRE ATLAS into their security programmes?

1. Familiarisation and Training: Encourage your security and AI/ML teams to familiarise themselves with the ATLAS framework. Workshops and internal training sessions can be incredibly beneficial.

2. AI Threat Modelling Workshops: Conduct dedicated threat modelling sessions for your key AI/ML applications, using ATLAS as your guiding framework. This helps identify critical attack paths and potential vulnerabilities unique to AI.

3. Security Control Mapping: Map your existing and planned security controls against ATLAS techniques. Identify gaps where your current defences might not adequately address AI-specific threats.

4. Develop AI-Specific Detection Rules: For your Security Operations Centre (SOC) or monitoring tools, develop detection rules based on the Indicators of Compromise (IOCs) or anomalous behaviours described for various ATLAS techniques.

5. Collaborate with Data Scientists and ML Engineers: ATLAS provides a common ground for security professionals to engage more effectively with their data science and ML engineering counterparts, fostering a shared understanding of risks and responsibilities.

The Road Ahead

As AI becomes increasingly pervasive in our organisations, understanding and defending against AI-specific threats is no longer optional; it’s a strategic imperative. MITRE ATLAS provides us with an indispensable tool for this journey. By systematically applying its insights, we can move beyond reactive defence to build AI systems that are designed with resilience at their core, capable of withstanding the complex and evolving tactics of today’s adversaries.

Keep building securely, and let’s make our AI systems truly robust.