Securing the Future: A First Look at SANS Critical AI Security Controls v1.1
Introduction
Artificial intelligence is no longer experimental—it’s embedded in production workflows, critical infrastructure, and high-stakes decision-making. But while AI evolves rapidly, security practices have struggled to keep pace. Enter the SANS Critical AI Security Controls (v1.1)—a new framework aiming to establish foundational, adaptable, and actionable controls for securing AI systems.
This blog breaks down the key components of the SANS draft, explains their relevance, and suggests first steps organizations can take today.
Why AI Security Now?
Recent incidents—from prompt injection attacks to data poisoning and model manipulation—highlight that traditional controls are not sufficient for modern AI pipelines. Regulatory efforts (like the EU AI Act and the NIST AI Risk Management Framework) signal a global shift toward enforceable accountability in AI use.
The SANS Critical AI Controls are an important step in providing technical and operational guidance for defending against these emerging threats.
The Structure of the Controls
SANS categorizes its AI security guidance into Four Pillars:
AI Governance
Establishing oversight, accountability, and responsible use of AI.- Define ownership and roles
- Assess legal and ethical risks
- Track provenance and purpose of AI models
AI System Development
Securing the model lifecycle, from data to deployment.- Secure training datasets
- Control input sanitization
- Ensure reproducibility and change management
AI System Operations
Protecting deployed models and ongoing AI services.- Implement access controls
- Monitor model drift
- Prevent abuse through usage limits and throttling
AI Incident Detection & Response
Preparing for and reacting to AI-specific attacks.- Detect anomalous output
- Create AI-specific playbooks
- Log and analyze interactions (e.g., prompts, API usage)
Mapping to Security Maturity
| Maturity Level | Indicators |
|---|---|
| Ad Hoc | No designated AI owner, no security testing of models |
| Basic | Controls applied inconsistently, often manually |
| Standardized | Consistent secure development, minimal model monitoring |
| Managed | Centralized visibility, risk-informed deployments |
| Adaptive | Continuous risk assessment, red teaming, AI-specific threat modeling |
Sample Controls in Practice
Let’s walk through how a mid-sized SaaS company using generative AI in customer service could apply several of these controls:
- Governance: Assign an “AI Security Lead” responsible for prompt risk assessments on new model deployments.
- Development: Use hashed training data snapshots for reproducibility and auditability.
- Operations: Limit token length and request frequency per API key to mitigate prompt abuse.
- Detection & Response: Use anomaly detection to flag model hallucinations or inconsistent behavior, then route logs to Splunk for triage.
Practical Next Steps
Organizations interested in adopting the SANS Critical AI Controls should consider:
Running an AI System Inventory Audit
What models, datasets, APIs, or vendors are currently used? Who owns them?Starting Small with Governance
Define who is responsible for AI oversight—this is your entry point to accountability.Developing a Baseline AI Threat Model
Focus on your highest-risk systems (e.g., public-facing LLMs or decision-support tools).Testing Controls with a Pilot Use Case
Apply selected controls (e.g., input filtering, output logging) to one project and evaluate results.
Conclusion
The SANS Critical AI Security Controls v1.1 provide the kind of practical guidance many organizations need right now: vendor-agnostic, layered, and actionable. While still a draft, this framework bridges a critical gap between conceptual AI ethics and operational security reality.
Expect updates to follow, but don’t wait to start.
In tomorrow’s post, we’ll dive into how to map these controls to the NIST AI Risk Management Framework for deeper compliance integration.
Stay secure. Stay intelligent.
Disclaimer:
This blog post references and summarizes content from the draft version of the SANS Critical AI Security Controls v1.1 for educational and informational purposes. All credit for the original framework belongs to the SANS Institute. Readers are encouraged to consult the official document for full details.