Singulr AI Glossary

AI SecOps — short for AI Security Operations — is the discipline of operationalizing security monitoring, threat detection, and incident response specifically for AI systems. It extends traditional security operations practices to cover the unique risks that AI models, agents, and applications introduce into an organization's technology environment. AI SecOps matters because AI systems don't fit neatly into existing security operations playbooks. Traditional SecOps monitors network traffic, endpoint behavior, and application logs for known threat patterns. AI systems introduce new categories of threats — prompt injection, model manipulation, data exfiltration through model outputs, and unauthorized agent actions — that require specialized detection capabilities and response procedures. AI SecOps typically involves several operational functions. Continuous monitoring tracks AI model behavior, agent actions, and data access patterns in real time. Threat detection uses rules and anomaly detection to identify attacks like prompt injection, jailbreak attempts, or unusual data access patterns. Incident response defines the procedures for investigating and containing AI-specific security events — such as an agent that's been manipulated into accessing restricted data or a model that's leaking sensitive information in its outputs. Alert triage and escalation workflows help security teams prioritize AI security events alongside traditional threats. For enterprises, AI SecOps is where AI governance meets day-to-day security operations. It's the operational muscle that ensures the policies defined by governance teams are actually enforced in production. As organizations scale their AI deployments, building AI SecOps capabilities — or extending existing SOC teams to cover AI threats — becomes a critical requirement.