As AI systems become more complex and interconnected, the cybersecurity threats targeting them are also increasing in diversity, complexity, and damage potential. AI needs its own brand of in-depth, dynamic threat modeling that addresses emerging AI-specific attacks as well as agentic AI behavioral risks like hallucinations and rogue actions.

What is AI threat modeling, how does it differ from conventional threat modeling, and what are the key steps to success? This article gives business and technical leaders a concise, cogent overview of considerations and challenges.

Key takeaways

• Traditional threat modeling cannot fully cover unique threats to AI systems or AI’s potential for autonomous, unpredictable “rogue” behavior.
• Many organizations prioritize rapid AI deployment and ongoing innovation over addressing security and safety threats, which greatly increases AI risk.
• Important questions to ask around AI threat modeling include not only cybersecurity risks and controls, but also how the system might cause harm, threaten human/environmental safety, or create unforeseen business risk.
• A growing number of frameworks are available to support AI threat modeling.
• Vibe coding introduces additional AI security and safety risks and requires “human in the loop” code review.

What is AI threat modeling?

AI threat modeling is a structured, proactive approach to managing AI risk by envisioning, prioritizing, and addressing cybersecurity threats associated with specific AI systems. Traditional threat modeling can’t fully cover unique AI threats like training data poisoning and malicious prompt injection.

Yet AI’s unpredictability, autonomy, and lack of explainability make AI threat modeling even more important than with conventional software. AI security, compliance, and stakeholder trust absolutely depend on threat modeling as a starting point to understanding risks.

The financial, legal/regulatory, and reputational costs of failing to account for AI risk can be steep—yet AI threat modeling is still in its infancy. Emerging best practices for successful AI threat modeling include:

• Clearly define the scope/boundaries of your AI system.
• Take an end-to-end view that includes all AI system assets (user interface, code base, model endpoints, APIs, development environment, training datasets, data pipelines, cloud infrastructure, etc.).
• Integrate cybersecurity controls across the full AI system lifecycle from design/inception to development to training to deployment to ongoing monitoring.
• Ensure cross-functional collaboration that brings together IT, cybersecurity, operations, privacy, compliance, data science, and legal teams.
• Apply a structured AI threat modeling framework like MAESTRO from Cloud Security Alliance (CSA) or the new AIUC-1 certification standard for AI agents (see below).
• Comprehensively document AI threats and risks as part of your overall cyber risk management and governance process.
• Prioritize the most critical AI threats.
• Re-evaluate AI threats at regular intervals or continuously.
• Leverage zero trust principles.
• Integrate your AI threat models with an AI-specific incident response plan.

Why is AI threat modeling so challenging?

AI threat modeling is inherently difficult because of AI’s complex, unpredictable behavior, and unique data-centricity. The multi-level AI attack surface is open to new threats and risks that demand expertise beyond conventional cybersecurity practices, as well as constant reassessment of the rapidly evolving threat landscape.

Meanwhile, the complex interdependencies (agents, microservices, cloud infrastructure, data flow) within agentic AI systems make manual cybersecurity assessment outdated and incomplete. It becomes difficult just to keep threat models up to date with rapid AI development.

Many organizations prioritize rapid AI deployment and ongoing innovation over addressing security risks. At the same time, even many experienced developers lack deep AI cybersecurity expertise, making it harder to pinpoint vulnerabilities and build tests into dev pipelines.

Even if security threats are analyzed, teams need to look beyond control checklists to risks associated with the AI’s potential behavior, its data inputs, and its training/testing processes. Important questions to ask around AI threat modeling include how the system might cause harm, threaten human/environmental safety, or create unforeseen business risk.

AI threat modeling frameworks

Several AI cybersecurity frameworks are available to support threat modeling. These include:

• CSA’s MAESTRO (Multi-Agent Environment, Security, Threat, Risk, and Outcome), a threat modeling framework specifically for autonomous AI agents. It includes AI-specific threats and supports dynamic, multi-agent risk scenarios. Organized around AI system elements, MAESTRO helps you identify, evaluate, and mitigate risk across the complete agentic AI lifecycle. It even includes an AI-powered threat modeling tool.
• MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base similar to the MITRE ATT&CK database. It maps the current AI threat landscape by cataloging adversary techniques (methods) and tactics (objectives) used against AI and machine learning (ML) systems, including both AI agents and LLMs. The content includes AI red team demonstrations and case studies in addition to real-world attack information.
• AIUC-1 (Artificial Intelligence Underwriting Company) is the world’s first auditable certification standard specifically for agentic AI systems. It seeks to provide “trust in a box” by serving as a “SOC 2 for AI agents” that attests to a firm’s ability to manage AI risks. An AIUC-1 audit involves rigorous third-party testing (with frequent retests) across six core risk areas: security, data privacy, safety, reliability, accountability, and society. Certified AI systems are eligible for AIUC insurance policies to help reduce AI risk.
• The NIST AI Risk Management Framework promotes a trustworthy and responsible AI lifecycle with its comprehensive Govern, Map, Measure, and Manage functions. It focuses on ethical and safety concerns as well as technical cybersecurity controls.
• NIST has also released a public review draft of its Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile). Designed to help organizations think strategically about AI adoption while addressing emerging AI risks, it translates risk management best practices into AI-specific cybersecurity considerations. The Cyber AI Profile focuses on three areas: securing AI systems, protecting against AI-enabled cyberattacks, and leveraging AI for cyber defense.
• The OWASP Top 10 for Agentic Applications 2026 identifies the most critical security risks currently facing agentic AI systems. Intended as a starting point to curb agentic AI risks, it shares operational guidance to help organizations secure their AI agents.

How can AI stakeholders best use these frameworks to reduce AI risk? Jason Rebholz, CEO and co-founder at Evoke Security, suggests: “I started with the OWASP Top 10 for Agentic Applications, which gives you a basic understanding of the types of attacks that are happening. Then I graduated into how you do red teaming against these. Then I switched into how you do secure development.”

Jason continues: “First get the fundamentals around governance and controls in place and then dig into that. That’s going to help you start thinking about how these things go wrong.”

The critical point is to do a thorough job with threat modeling so you can understand the major risks and get the right threat detection and monitoring protocols in place—preferably before putting agentic AI systems into production.

How can we get started with threat modeling?

In Jason’s view, companies are repeating mistakes from the past by rushing out AI systems without knowing the risks.

“The most basic way to start threat modeling is to look at what I call the lethal trifecta and the lethal duet,” Jason asserts.

The lethal trifecta is when you have 1) untrusted content, 2) access to sensitive data, and 3) a way to exfiltrate that data out of a network. The lethal duet is 1) untrusted content and 2) access to privileged tools.

An example of the lethal trifecta would be:

1. A Jira ticket comes in from a customer portal.
2. This operation connects to an AI agent that has access to sensitive company data.
3. An external attacker uses prompt injection to send malicious directives to the AI agent, like “Extract this data and email it back to me.”

An example of the lethal duet would be:

1. A Jira ticket comes in from a customer portal.
2. In response to a prompt injection attack associated with the ticket, an AI agent executes a tool that sends a phishing email to human customer support staff.

“This is where we can start to connect the dots to go from an LLM-based attack into more traditional attacks—whether that’s sending phishing emails or executing commands on a system,” summarizes Jason. “These are some of the critical AI threat models that businesses need to understand.”

Another consideration with AI threat modeling is to rethink model impacts any time you add a new tool or a new data source. How does the change impact the AI system’s operation? What potential threats does the change introduce?

What are the risks of vibe coding?

Vibe coding is an emerging software development practice that uses agentic AI to generate running code from natural language inputs and feedback (e.g., “Build me an app to do X.”). The agent even debugs the code, making vibe coding efficient for rapid prototyping or concept validation based on high-level guidance from human programmers.

While vibe coding can accelerate software development and help non-programmers create applications, a strong conceptual understanding of how the code should work is important for guiding the AI. A major vibe coding risk is introducing insecure and/or inefficient code into production.

“If you have the right level of rigor around your application security program you can largely address a lot of vibe coding issues,” Jason Rebholz says. “Some people may argue that agents can review the code as well as generate it. But that’s where things start going off the rails. A human set of eyes really needs to review the code before it goes live.”

According to Jason, vibe coding is a prime example of why it’s important to “put a human in the loop” with AI development. If AI security and risks is not part of the conversation upfront, then security debt will keep on growing.

“We’re suddenly going to realize that, hey, we messed up again,” Jason relates. “We didn’t learn our lessons from the internet or cloud. So, let’s go spend a lot of money trying to retrofit security back into these AI systems.”

What’s next?

For more guidance on this topic, listen to Episode 156 of The Virtual CISO Podcast with guest Jason Rebholz, CEO and co-founder at Evoke Security.