After AI Reaches Production: 12 Ways Security Teams Can Take Control

Security teams need more than visibility into AI applications, they need a repeatable framework for monitoring, investigating, and defending them in production. The post After AI Reaches Production: 12 Ways Security Teams Can Take Control appeared first on SecurityWeek .

The article outlines 12 operational security practices for AI applications in production, including visibility, telemetry, preventive and detective controls, investigation, mitigation, and continuous iteration to handle issues like abuse, fraud, and attacks against AI-powered systems.[1] It emphasizes integrating AI-specific telemetry and controls into existing security workflows so that security teams can monitor, investigate, and respond to threats targeting AI applications at runtime.[1][2] From a CyberSE.AI perspective, this reflects a primary risk of AI agent abuse in production environments, where insufficient monitoring and controls can allow malicious use, fraud, or unsafe autonomous actions by AI components. Practically, organizations should adopt continuous AI red teaming and secure build practices to stress-test AI workflows, validate logging and enforcement paths, and institutionalize a repeatable production security framework before and after AI systems go live.