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AI Security Best Practices

A comprehensive security playbook for building, deploying, and maintaining LLM applications that are resilient to prompt injection and other AI-specific threats.

Security-First Architecture

Design your AI application with security as a foundational requirement, not an afterthought:

  1. Threat Modeling

    Before writing code, identify all the ways an attacker could abuse your AI system. Map out data flows, trust boundaries, and potential attack surfaces.

  2. Principle of Least Privilege

    Give the model access only to the tools and data it absolutely needs. Separate read and write permissions. Require escalation for sensitive operations.

  3. Trust Boundaries

    Treat all user input and retrieved content as untrusted. Sanitize data as it crosses trust boundaries, just like you would in traditional web application security.

  4. Fail Secure

    When uncertain, deny the request. When an error occurs, fail in a way that does not expose sensitive information or grant unauthorized access.

Security Checklist

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Pre-Deployment Security Checklist:
  • Input sanitization pipeline is implemented and tested
  • Output filtering is active for all response paths
  • System prompt is hardened against override attempts
  • Tool access follows least-privilege principles
  • Human approval is required for high-impact actions
  • Rate limiting is configured per user and globally
  • Injection test suite passes with acceptable ASR
  • PII detection and redaction is active on inputs and outputs
  • Monitoring and alerting are configured
  • Incident response plan is documented and rehearsed
  • Canary tokens are embedded for leak detection
  • Rollback and kill switch procedures are tested

Incident Response for AI Systems

Phase Actions Timeline
Detection Automated alerts from monitoring, user reports, security team discovery Minutes
Triage Classify severity, assess scope of impact, identify affected users Minutes to hours
Containment Block attack vector, enable additional filtering, reduce model capabilities if needed Hours
Eradication Fix root cause (update prompt, add filter rule, patch vulnerability) Hours to days
Recovery Restore full service, verify fix effectiveness, update test suite Days
Lessons Learned Document incident, update threat model, improve defenses Week

Staying Ahead of Threats

Follow Research

Track publications from AI security researchers, OWASP LLM Top 10 updates, and industry reports on emerging attack techniques.

Update Regularly

Refresh your attack test suite monthly. New jailbreak techniques emerge constantly, and defenses must evolve to match.

Share Intelligence

Participate in industry information-sharing groups. Report new attack techniques and defense bypasses to help the community.

Red Team Continuously

Schedule regular red team exercises. Rotate team members to bring fresh perspectives and avoid blind spots.

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Final Thought: Perfect security against prompt injection is not currently achievable. The goal is to make attacks as difficult as possible, limit their impact when they succeed, detect them quickly, and respond effectively. This is the same approach that has served traditional cybersecurity for decades.