Security discussions must pivot from who has which permissions to what data an AI agent touches and whether its actions stay within its authorized scope, since current tools often fail to reveal agent-level behavior across systems.

Financial services firms, despite investing heavily in security tools, faced SaaS supply chain attacks at a rate notably above the cross-industry average, underscoring gaps in defenses.

A Consensuswide survey of 500 U.S. CISOs found that nearly every organization saw at least one security incident tied to their SaaS or AI ecosystem in 2025, with only a single respondent reporting zero incidents.

Even with claimed OAuth token governance and real-time data flow mapping, breaches via compromised tokens and the inability to observe what data AI tools exchange reveal a governance gap between configuration and runtime activity.

Despite plans to boost SaaS and AI security spending in 2026, simply adding more tools without changing architecture won’t close the gap; experts advocate shifting to runtime monitoring and behavior-based baselines.

AI agents operate with real-time goal-directed decisions, which makes security monitoring harder because agent behavior is not easily classified as normal or abnormal like traditional automation.

Security monitoring should evolve from configuration auditing to runtime monitoring, implementing real-time token governance tied to actual usage and enabling a forensic timeline of agent activity across connected systems after incidents.

Traditional security tools are built for front-door patterns and fail to capture the full activity of AI agents moving data across multiple services, limiting visibility.

Most ITDR and non-human identity tools stop at authentication, not at what data is queried, moved, or sent by agents, and two-thirds of CISOs say distinguishing human vs. non-human behavior is a major limitation.