AI Security Overview

Summary

OWASP's AI Security Overview delivers a comprehensive technical framework specifically designed for security practitioners navigating the complex landscape of AI system threats. Unlike generic risk management frameworks, this resource provides actionable security controls mapped directly to AI-specific attack vectors like model poisoning, adversarial inputs, and data exfiltration. What sets this apart is its practical, hands-on approach—developed by the same organization behind the renowned OWASP Top 10, it translates traditional cybersecurity expertise into the AI domain with concrete implementation guidance.

The OWASP Difference: Why This Framework Stands Out

OWASP brings two decades of web application security expertise to AI systems, creating a bridge between traditional cybersecurity and emerging AI threats. This isn't theoretical risk modeling—it's battle-tested security thinking applied to machine learning pipelines, model deployment, and AI data flows. The framework leverages OWASP's proven methodology of identifying, categorizing, and mitigating security risks, now specifically tailored for AI environments where traditional security controls may fall short.

The integration with OpenCRE (Common Requirement Enumeration) means these AI security requirements can be directly mapped to existing security standards like ISO 27001, NIST Cybersecurity Framework, and regulatory compliance requirements—a crucial advantage for organizations already operating within established security governance structures.

Core Security Domains Covered

AI Model Security: Protection against model theft, reverse engineering, and intellectual property leakage. Includes specific controls for model versioning, access controls, and secure model storage.

Training Data Protection: Safeguarding sensitive training datasets from unauthorized access, inference attacks, and privacy violations. Addresses data lineage, anonymization techniques, and secure data handling throughout the ML lifecycle.

Inference-Time Threats: Real-time protection against adversarial inputs, prompt injection, and model manipulation during production deployment. Covers input validation, output sanitization, and anomaly detection.

AI Supply Chain Security: Securing third-party models, pre-trained weights, and external AI services. Includes vendor risk assessment, model provenance verification, and dependency management.

Infrastructure Security: Traditional cybersecurity controls adapted for AI workloads, including secure containerization, API security, and cloud-specific AI service protections.

Who This Resource Is For

Security Engineers and Architects looking to extend existing security programs to cover AI systems, particularly those already familiar with OWASP methodologies and seeking AI-specific threat models.

DevSecOps Teams responsible for securing ML pipelines and AI deployment infrastructure who need concrete security controls that integrate with existing CI/CD processes.

Compliance Officers in regulated industries who must demonstrate security controls for AI systems and need mappings to established security frameworks and standards.

AI Platform Engineers building internal ML platforms who require comprehensive security guidance that goes beyond basic access controls to address AI-specific attack vectors.

CISOs and Security Leaders evaluating AI security posture and needing a structured framework to assess risks and implement appropriate controls across the AI lifecycle.

Implementation Roadmap

Start with the threat modeling exercises to identify which AI security domains are most relevant to your specific use cases. The framework provides risk assessment matrices that help prioritize security controls based on your threat landscape and risk tolerance.

For organizations with existing OWASP implementations, begin by extending current security testing practices to include AI-specific scenarios. The framework includes testing methodologies that can be integrated into existing security validation processes.

Plan for the OpenCRE integration to automatically map AI security requirements to your current compliance obligations—this feature will significantly reduce the overhead of demonstrating AI security compliance across multiple standards and regulations.

Watch Out For: Common Implementation Gaps

Many organizations focus heavily on model accuracy and performance while treating security as an afterthought. This framework emphasizes security-by-design principles that must be integrated from the beginning of AI development cycles.

Don't underestimate the complexity of AI supply chain security—third-party models and pre-trained weights can introduce significant vulnerabilities that traditional software composition analysis tools may miss.

The rapidly evolving nature of AI threats means this framework requires regular updates and reassessment. Unlike traditional security controls that remain stable for years, AI security controls may need quarterly review and adjustment as new attack techniques emerge.