As AI systems become more sophisticated—from predictive and generative to fully agentic—the attack surface expands faster than traditional security can keep up. This white paper provides a practical, framework-driven approach to Secure AI by Design, guiding organizations through the shift from reactive defenses to proactive, integrated AI security.
You’ll learn how to implement security from inception through deployment—addressing vulnerabilities unique to AI’s probabilistic and autonomous nature. Drawing from CISA’s Secure by Design principles and a Defense-in-Depth strategy, this guide shows how to embed protection at every phase of the Machine Learning Security Operations (MLSecOps) lifecycle.
In this white paper, you’ll discover:
- The Evolution of AI Threats: Understand how data poisoning, model deserialization, and prompt injection attacks expose the weaknesses of modern AI systems.
- Core Secure AI by Design Principles: Learn how to apply CISA’s foundational security pillars—ownership, transparency, and leadership—to AI development and operations.
- Essential Frameworks for Implementation: See how the 2025 OWASP Top 10 for LLMs and GenAI, MITRE ATLAS, and NIST AI-RMF align to provide actionable, end-to-end AI security guidance.
- Defense-in-Depth in Practice: Map security controls across the MLSecOps lifecycle to prevent and detect AI-specific threats.
- Next-Generation AI Security Tools: Explore the capabilities that traditional cybersecurity misses, from model scanners and AI vulnerability feeds to AI-aware access controls, red teaming, and agentic AI monitoring.
Whether your organization is deploying predictive models or autonomous agents, this white paper offers a complete strategy for building and maintaining AI systems that are secure, compliant, and resilient by design.
