Abnormal AI has released its 2026 State of Behavioral Security Report, revealing that 46% of security professionals say they are not at all effective at establishing what normal behaviour looks like for AI agents, even as 43% say agents will receive their greatest behavioural-monitoring investment over the next 24 months, more than three times that of API integrations (14%).
Behavioural security uses AI to learn what is normal for each identity, account, and system, then flags activity that deviates, rather than relying on known threat signatures.
More than 81% of respondents now rely on behavioural anomaly detection to a major or extensive degree, but that maturity has not yet extended to AI agents or machine identities.
The data shows that many teams are operating without a clear picture of normal. While 72% of respondents say they are better prepared to detect anomalous behaviour from non-human identities than from human ones, 53% do not define normal behaviour for machine identities at all. And even when anomalies are caught, response is slow: 90% say knowing why an anomaly triggered is critical, but 51% need four hours or more to investigate and remediate one.
Methodology
Abnormal AI’s 2026 State of Behavioral Security Report is based on a survey of 525 cyber security professionals at organizations with 1,000+ employees across six countries, approximately 85% of whom hold CISO-level or security operations leadership roles. The survey was fielded by AimPoint Group on behalf of Abnormal AI between July and August 2026. Results reflect respondents’ self-reported views.
Resilience Forward comment
While the survey focuses on the cyber security implications of not having a baseline for AI agent behaviour, the absence of these metrics poses a severe threat to organizational resilience.
Agentic AI misbehaviour is rarely binary. Unlike traditional IT systems, which typically fail by crashing or going offline (a clear, measurable loss of availability), AI agents often experience ‘silent failures’. They continue to operate, but their logic drifts, their decision-making degrades, or they begin hallucinating outcomes. In conventional business continuity terms, the system appears entirely healthy, yet the operational integrity of its output is compromised.
This presents a profound resilience challenge. Because agentic AI operates autonomously — often feeding outputs directly into other automated systems without human verification — a deviation from normal behaviour can trigger compounding errors at machine speed.
If an organization cannot define what a ‘normal’ decision boundary or process outcome looks like for its AI, it cannot detect when the agent begins operating incorrectly. Ultimately, AI resilience cannot be managed through traditional business continuity or uptime metrics.
Establishing a rigorous baseline for acceptable agent behaviour, monitoring for logical drift, and implementing automated circuit breakers when an agent exceeds its parameters need to be non-negotiable prerequisites for safely integrating autonomous systems into critical business services.
