AI cybersecurity recommendations emphasize securing products across their lifecycle, from design to sustainment. Key practices involve identifying governance, assessing AI risks, and implementing robust testing strategies. These include testing for prompt injection, sensitive information disclosure, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, unbounded consumption, adversarial AI, jailbreaks, and model extraction. From requirements to discontinuation, these measures ensure comprehensive security, safeguarding AI systems against evolving threats and vulnerabilities throughout their development lifecycle.