Are you stepping up your AI Security with S.T.A.I.R. Threat Modeling?

Picture yourself as a Gandalf from The Lord of the Rings, not with a glowing staff but instead armed with the power of algorithms and neural networks to defend an evolving and expanding digital realm filled with AI-powered systems.

Welcome to the epic saga of AI threat modeling - a quest that includes heroic efforts and noble goals like an Arthurian legend, future-thinking, fast-evolving cutting-edge challenges and high-stakes dilemmas like a cyberpunk thriller, and a sprinkle of Shakespearean drama with elements of profound questions and tension.

While traditional threat modeling feels like fortifying a medieval castle (think moats, drawbridges, and a grumpy knight at the gate), AI threat modeling is more like taming a mischievous dragon. It’s powerful, unpredictable, and capable of great good or great chaos.

AI threat modeling is more like taming a mischievous dragon

Let’s explore how AI threat modeling blends the old with the new and unveils practical steps to secure your AI systems.

Are Threat Modeling AI systems any different?

At its core, threat modeling AI or non-AI systems are built on timeless principles: managing risk by proactively identifying and addressing vulnerabilities that somebody can exploit and devalue your assets.

General guidelines include the following:

Map the System Like Da Vinci

Leonardo da Vinci approached art and science with equal curiosity. Take a similar approach: document your AI system’s data flows, dependencies, and potential vulnerabilities.

Think Like a Villain, Build Like a Hero

Imagine you’re Tony Stark, designing Iron Man’s suit while preparing for every conceivable enemy. How would you exploit the system if you were a bad actor? Then, build defenses against those scenarios.

This foundation remains unchanged, but the AI dragon introduces new twists and turns to consider.

Data: Treasure or Trojan Horse?

Traditional threat modeling generally assumes structured data flows. In contrast, AI systems require modeling for risks like prompt injections, hallucinations, and data poisoning because AI systems often deal with user-generated content or dynamic APIs as inputs, making data flow unpredictable.

The unpredictability of data flows in AI systems warrants a more dynamic approach to threat modeling AI systems

AI thrives on data, but bad actors can poison the well. Imagine Snow White’s poisoned apple - it looked perfect but packed a punch. Similarly, poisoned datasets can corrupt AI models, creating systems that output harmful or skewed results. While traditional systems consider static assets and roles, AI systems add complexity by requiring context-aware threat analysis, e.g., LLMs may interpret inputs differently based on context, potentially leaking sensitive information.

Here are some practical defensive steps you can take:

1. Classify sensitive data, so you know where it’s stored and how it is used.
2. Encrypt sensitive non-public data so that it is not useful to the attacker if stolen.
3. Use multi-factor authentication (MFA) and continuous monitoring to keep out cyber villains.
4. Incorporate different contexts as part of your threat model.

The Model: A Black Box with Secrets

In traditional systems, trust boundaries primarily delineate user-system and system-to-system interactions. However, these boundaries are more fluid for AI systems and depend on the sensitivity of prompts, outputs, and integrations with private data sources.

Trust boundaries in AI systems are more fluid and not quite as demarcating

If AI systems were characters, they’d be Dumbledore, wise but mysterious. Additionally, pre-trained models often come from third-party sources, making it difficult to trace their origins or predict their behavior. Worse, some might harbor hidden malware, like the Trojan Horse of ancient Greek lore.

Here are some practical defensive steps you can take:

1. Vet your models like Sherlock Holmes scrutinizing suspects and verifying their sources and integrity.
2. Scan for malware regularly, even if AI can have skeletons in its closet.
3. Restrict access with role-based controls, ensuring no one (or nothing) gains undue influence.

Operations: The Wild West of Interaction

Once deployed, AI systems interact with the world, and the world interacts back. Unlike static systems, generative AI introduces additional attack vectors, such as output manipulation and unauthorized access to inferred data. Threat modeling AI systems must account for the model's role in transforming or predicting sensitive data. Malicious users might exploit loopholes through prompt injection, model evasion, model inversion, and poisoning techniques, causing your AI to “jailbreak” and behave unexpectedly.

Threat modeling AI systems must account for the model's role in data transformations and predictions

As the wise saying goes, “Whoever walks in integrity walks securely” (Proverbs 10:9); it is essential that the AI models we build are threat-modeled for transparency and fairness to ensure that they operate as intended without biases or manipulation.

Here are some practical defensive steps you can take:

1. Monitor inputs and outputs to catch unusual activity.
2. Limit query rates to stop systems from being overwhelmed.
3. Build guardrails that block unsafe instructions while still allowing meaningful user interaction.

Take the S.T.A.I.R. to elevate AI security!

In the AI world, I'd like to propose the threat modeling process designed to elevate your security posture while embedding AI into the core of your process. Each step on the S.T.A.I.R. should take you closer to better identifying, analyzing, and mitigating risks.

S: Spot Threats

Like Sherlock Holmes scanning a crime scene, the first step is identifying potential threats to your AI system.

• Look for vulnerabilities in data pipelines, model integrations, and user interactions.
• Spot adversarial risks like data poisoning, model theft, or prompt injection attacks.
• Think broadly—consider internal risks, external attacks, and regulatory non-compliance to identify trust boundaries.

T: Track Assets

Once you know what could go wrong, inventory the assets you must protect.

• Define your critical assets: training data, AI models, APIs, and the operational environment.
• Classify assets by their sensitivity and importance.
• Use tools like asset management systems to monitor changes over time.

A: Analyze Risks

With threats and assets identified, evaluate how they intersect.

• Weigh impact vs. likelihood: Which risks pose the greatest threat to your operations or reputation?
• Leverage traditional tools like STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege) and DREAD (Damage Potential, Reproducibility, Exploitability, Affected Users, Discoverability) or Process for Attack Simulations and Threat Analysis (PASTA) for systematic risk assessment.
• Map risks to their potential consequences, from data breaches to biased outputs.

I: Implement Controls

Now comes the critical action step: mitigate the risks.

• Harden your systems with access controls, encryption, and secure development practices.
• Use AI-specific guardrails, like adversarial testing and anomaly detection.
• Secure the AI supply chain by vetting models and third-party integrations.

R: Respond and Monitor

Finally, build resilience by preparing for incidents and staying vigilant.

• Set up monitoring tools for real-time threat detection.
• Create a response plan for breaches or misuse, with roles and responsibilities clearly defined.
• Continuously update your defenses as threats evolve, and your AI systems learn and adapt.

This methodology emphasizes a progressive, structured approach to threat modeling, guiding teams from spotting threats to monitoring systems while entrenching the addressing of AI risks at every step. It’s easy to remember and hard to ignore and can help your cybersecurity journey get an upgrade.

The FinAI_ Word: The Threat Modeling Commandment

So the next time you face your AI dragon, remember that a combination of adapted threat modeling, fitting controls, ingenious strategies, and maybe a hint of humor can go a long way. As the wise old Gandalf would say, “Thou shalt not pass insecure AI applications into production without threat modeling them first!”

PS:

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Works Cited

AI Threat Modeling: A Proactive Approach to Securing AI Systems.” Snowflake, 2023, www.snowflake.com/guides/ai-threat-modeling/.

Cortegaca, Danny, et al. “Threat Modeling Your Generative AI Workload to Evaluate Security Risk.” Amazon Web Services, 18 Nov. 2024, aws.amazon.com/blogs/security/threat-modeling-your-generative-ai-workload-to-evaluate-security-risk/. Accessed 3 Dec. 2024.

IBM Technology. “How to Secure AI Business Models.” YouTube, 7 Nov. 2023, www.youtube.com/watch?v=pR7FfNWjEe8.

Marshall, Andrew, et al. “Threat Modeling AI/ML Systems and Dependencies.” Learn.microsoft.com, 2 Nov. 2022, learn.microsoft.com/en-us/security/engineering/threat-modeling-aiml.

Yang, Shuiqiao, et al. “ThreatModeling-LLM: Automating Threat Modeling Using Large Language Models for Banking System.” ArXiv.org, 26 Nov. 2024, arxiv.org/abs/2411.17058.