Summary:

AI-powered Large Language Models (LLMs) are revolutionizing industries, but they also introduce new security risks, including prompt injection, data poisoning, and adversarial attacks. While traditional cybersecurity methods provide some protection, AI-specific frameworks offer better alignment for securing LLMs.

This guide explores three major security frameworks—MITRE ATT&CK for ICS, CISCO’s AI Security Model, and OWASP’s AI Security Tooling—to help businesses build a structured approach to securing AI models. Additionally, we provide a tactical checklist that organizations can use to start implementing AI security best practices today.

Why AI LLM Security Matters

AI LLMs, such as OpenAI’s GPT, Meta’s Llama, and Google’s Gemini, are being integrated into business operations at an unprecedented rate. However, they come with unique risks:

✅ Prompt Injection Attacks – Attackers manipulate LLMs into leaking sensitive data or executing unintended commands.
✅ Data Poisoning – Compromising training data to alter model behavior.
✅ Adversarial Inputs – Crafting inputs that trick AI into providing incorrect or harmful responses.
✅ Model Theft & API Abuse – Unauthorized use or replication of AI models.

Without a structured security approach, organizations leave their AI models vulnerable to manipulation, breaches, and regulatory violations.

Applying Security Frameworks to AI LLMs

1. MITRE ATT&CK for ICS: Adapting Industrial Security for AI

MITRE ATT&CK for Industrial Control Systems (ICS) focuses on detecting cyber threats in operational environments. While designed for industrial systems, its methodology applies well to AI security by helping organizations model potential attacks on LLMs.

Key MITRE-Based Tactics for AI Security:

• Reconnaissance → Identify vulnerabilities in AI models (e.g., API endpoints)
• Initial Access → Prevent unauthorized access via authentication and rate limiting
• Execution → Limit AI execution to prevent harmful prompts and unauthorized code execution
• Exfiltration → Monitor for data leakage from AI responses

✅ Tactical Tip: Use MITRE’s tactics to simulate adversarial attacks and test your AI defenses.

2. CISCO’s AI Security Model: Applying Zero Trust to LLMs

CISCO’s AI security framework builds upon Zero Trust principles, emphasizing:

• Identity & Access Management (IAM) → Who can access the AI model?
• Data Integrity & Monitoring → Is the training data safe from manipulation?
• Endpoint Protection → Are APIs and model outputs secured from abuse?

✅ Tactical Tip: Apply Zero Trust to AI access, ensuring strict authentication, continuous monitoring, and network segmentation for AI-driven applications.

3. OWASP’s AI Security Tooling: Addressing AI-Specific Vulnerabilities

OWASP (Open Web Application Security Project) is known for securing web applications, but its AI Security Guidelines are now crucial for protecting LLMs.

Key AI Risks from OWASP:

• Data Supply Chain Risks → Ensure AI training data is vetted and protected.
• Prompt Injection Prevention → Sanitize user inputs and set strict policies.
• API Security → Restrict AI model access through proper authentication controls.

✅ Tactical Tip: Use OWASP’s Top 10 AI Risks as a baseline for securing AI applications.

High-Level Checklist for Securing AI LLMs

Conclusion: A Tactical Approach to AI Security

Rather than treating AI security as an afterthought, organizations must proactively adopt structured methodologies to protect their models. Leveraging MITRE ATT&CK, CISCO’s AI Security Model, and OWASP AI guidelines provides a solid foundation for securing LLMs.

This guide offers immediate tactical steps to safeguard AI applications, from securing APIs and data pipelines to implementing Zero Trust. By applying these strategies, businesses can mitigate AI-related risks and ensure responsible AI deployment.