Introducing AI Security Frameworks You Need to Know: Mitre Atlas and OWASP Top 10 LLM

Myra Roldan
2 days ago
4 min read

Artificial intelligence is transforming industries fast. But with great power comes great risk. AI systems, especially large language models (LLMs), face unique security challenges that can expose your organization to threats. You need clear, practical frameworks to protect your AI investments and keep your data safe.

Two leading AI security frameworks are gaining traction: Mitre Atlas and the OWASP Top 10 for Large Language Model Applications. These frameworks help you identify, assess, and manage AI risks effectively.

This post breaks down what these frameworks offer and how you can use them to build secure, trustworthy AI systems. You’ll also see how AI security tools like AI Security Suite can support your efforts.

Eye-level view of a digital dashboard showing AI security metrics

AI security dashboard displaying risk metrics and threat analysis

What Is Mitre Atlas and Why It Matters

Mitre Atlas is a comprehensive framework designed to map AI risks and attacks. It provides a structured way to understand how AI systems can be exploited and what defenses work best.

Key Features of Mitre Atlas

Attack Taxonomy: Atlas categorizes AI attacks by type, such as data poisoning, model evasion, and extraction attacks. This helps you recognize threats specific to AI.
Defense Strategies: It pairs each attack type with recommended defenses, giving you actionable steps to protect your AI models.
Use Cases and Examples: Atlas includes real-world scenarios showing how attacks happen and how to respond.

Why Use Mitre Atlas

Mitre Atlas is valuable because it focuses solely on AI security. Unlike traditional cybersecurity frameworks, it addresses AI-specific risks that standard tools miss. This makes it essential for enterprises deploying AI at scale.

By using Atlas, you can:

Identify AI vulnerabilities early
Prioritize security efforts based on attack likelihood
Build defenses tailored to your AI models

Understanding the OWASP Top 10 for Large Language Model Applications

The OWASP Top 10 LLM project targets security risks in large language model applications. It’s a community-driven list highlighting the most critical threats to LLMs.

The Top 10 Risks Include

Data Leakage: Sensitive information exposure through model outputs
Prompt Injection: Manipulating model behavior via crafted inputs
Model Theft: Unauthorized copying or extraction of model parameters
Adversarial Inputs: Inputs designed to confuse or mislead the model

How OWASP Helps You

OWASP provides clear descriptions of each risk, examples of attacks, and mitigation strategies. This makes it easier to:

Understand common LLM threats
Train your teams on AI security best practices
Implement controls to reduce risk

The OWASP Top 10 LLM is especially useful if you build or deploy chatbots, virtual assistants, or other LLM-powered tools.

Close-up view of a computer screen showing code with AI security annotations

Code snippet annotated with AI security risks and mitigation notes

How to Use These Frameworks Together

Mitre Atlas and OWASP Top 10 LLM complement each other. Atlas offers a broad AI security taxonomy, while OWASP drills down into LLM-specific risks.

Here’s how to combine them:

Start with Atlas to map your AI system’s attack surface. Identify which attack types apply to your models.
Use OWASP Top 10 LLM to focus on language model risks if you use LLMs in your applications.
Apply recommended defenses from both frameworks to build layered security.
Continuously monitor and update your security posture as new threats emerge.

Practical Steps to Secure Your AI with These Frameworks

You can’t protect what you don’t measure. Here’s a simple plan to get started:

Assess your AI assets: Identify all AI models and data flows in your organization.
Map threats using Mitre Atlas: Use the attack taxonomy to find vulnerabilities.
Review OWASP Top 10 LLM risks: Check if your LLM applications face these threats.
Implement controls: Use techniques like input validation, output filtering, and access controls.
Train your team: Educate developers and security staff on AI risks and mitigation.
Use AI security tools: Platforms like AI Security Suite provide automated risk detection and compliance checks aligned with these frameworks.

How AI Security Suite Supports Framework Adoption

Managing AI security manually is tough. That’s where tools like AI Security Suite come in. This platform helps you:

Scan AI models for vulnerabilities based on Mitre Atlas and OWASP guidelines
Automate threat detection and alerting
Generate compliance reports for audits
Provide actionable recommendations to fix issues

Using such a tool accelerates your security efforts and ensures you follow best practices consistently.

Learn more about AI Security Suite here.

High angle view of a security operations center monitoring AI threats

Security operations center tracking AI threat alerts in real time

Final Thoughts on AI Security Frameworks

AI security is no longer optional. You must protect your AI systems from evolving threats to avoid costly breaches and maintain trust.

Mitre Atlas and OWASP Top 10 LLM give you clear, practical frameworks to identify and manage AI risks. Use them together to cover broad AI threats and LLM-specific challenges.

Pair these frameworks with tools like AI Security Suite to automate risk detection and strengthen your defenses. This approach helps you build secure, ethical, and scalable AI solutions that deliver real business value.

Start mapping your AI risks today and take control of your AI security future.

Disclaimer: This post is for informational purposes only and does not constitute legal or professional advice.