As artificial intelligence moves from experimental side projects to the core of the enterprise tech stack, the attack surface for modern organizations is expanding rapidly. AI workloads introduce unique risks—from "agentic" systems that can autonomously ship code to non-deterministic models vulnerable to prompt injection.
To help security teams keep pace, Datadog has outlined a comprehensive framework for AI security. Here are the essential best practices for securing AI from development to production.
1. Implement Runtime Visibility
Traditional security scanners often fall short in AI environments because they cannot account for the "live" behavior of autonomous agents. Effective security requires continuous runtime visibility. This allows teams to detect when an AI service begins making unauthorized API calls or minting secrets without human intervention. By monitoring the actual execution of AI workloads, organizations can catch cascading breaches before they move across the entire stack.
2. Hardening Against Prompt Injection and Toxicity
Unlike traditional software, AI models are susceptible to "behavioral" attacks.
Prompt Injection: Malicious inputs designed to bypass safety filters or extract sensitive data.
Toxicity Checks: Continuous monitoring of both prompts and responses to ensure the AI does not generate harmful, biased, or non-compliant content.
Using tools like Datadog LLM Observability, teams can perform real-time integrity checks to ensure models remain within their intended operational bounds.
3. Prevent Data Leakage with Advanced Scanning
AI models are only as good as the data they are trained on, but that data often contains sensitive information. Personally Identifiable Information (PII) or proprietary secrets can inadvertently leak into LLM training sets or inference logs.
Best Practice: Use a Sensitive Data Scanner (SDS) to automatically detect and redact sensitive information in transit. This is especially critical for data stored in cloud buckets (like AWS S3) or relational databases used for RAG (Retrieval-Augmented Generation) workflows.
4. Adopt AI-Driven Vulnerability Management
The sheer volume of code generated or managed by AI can overwhelm traditional security teams. To avoid "alert fatigue," organizations should shift toward AI-driven remediation:
Automated Validation: Use AI to filter out false positives from static analysis tools, allowing developers to focus on high-risk, reachable vulnerabilities.
Batched Remediation: Leverage AI agents to generate proposed code patches. This allows developers to review and apply fixes in bulk, significantly reducing the mean time to repair (MTTR).
5. Align with Global Standards
Securing AI shouldn't mean reinventing the wheel. Frameworks like the NIST AI Risk Management Framework provide a structured way to evaluate AI security. Modern security platforms now offer out-of-the-box mapping to these standards, helping organizations ensure their AI infrastructure meets compliance requirements for misconfigurations, unpatched vulnerabilities, and unauthorized access.
Conclusion
The shift toward "Agentic AI" means that a single mistake in a microservice can have far-reaching consequences. By combining traditional observability with specialized AI security controls, organizations can innovate with confidence, ensuring their AI transformations are as secure as they are powerful.