Notifications
Clear all
Topic starter
27/06/2026 1:37 pm
TL;DR: Discovery tools can find installed AI software, but they miss what identities are doing, which is how a Fortune 50 media breach led to 1.1TB stolen, including 44 million chat messages, over five months undetected, according to Abnormal AI. Behaviour-based governance now matters more than inventory because sanctioned or unsanctioned identities can both become high-risk access paths.
NHIMG editorial — based on content published by Abnormal AI: Key Insights on shadow AI governance and AI identity behaviour
By the numbers:
- In 2025, 1.27 million hardcoded AI-service credentials were exposed in public repos, roughly one every 25 seconds all year.
Questions worth separating out
Q: How should security teams govern shadow AI without relying on discovery alone?
A: Security teams should use discovery as the starting point, then combine it with runtime identity telemetry.
Q: Why do hardcoded AI credentials create more risk than ordinary code mistakes?
A: Hardcoded AI credentials are risky because they become standing identity paths when exposed, especially in public repositories.
Q: What do teams get wrong about AI agents and account takeover detection?
A: Teams often apply human-centric anomaly detection to machine actors, which misses how AI agents actually behave.
Practitioner guidance
- Correlate discovery with identity activity Pair AI tool discovery with logs for file access, API calls, and unusual session timing so sanctioned and unsanctioned behaviour are evaluated together.
- Treat exposed AI credentials as live identities Search public repositories, sample projects, and installer packages for hardcoded AI-service credentials, then revoke and rotate anything exposed before waiting for evidence of abuse.
- Baseline non-human behaviour separately from human users Build detection profiles that distinguish humans, service accounts, and AI agents so machine-speed access to unfamiliar systems is not automatically treated as noise or generic takeover activity.
What's in the full article
Abnormal AI's full analysis covers the operational detail this post intentionally leaves for the source:
- The article's breakdown of how the free AI art tool became the initial foothold in the Fortune 50 breach.
- The distinction between discovery-based shadow AI governance and runtime behaviour analysis for sanctioned identities.
- The product and engineering logic behind behavioural baselines for email and non-human identities.
- The source article's view of how the Abnormal Attune approach models identity behaviour across accounts.
👉 Read Abnormal AI's analysis of shadow AI governance and identity behaviour →
Shadow AI governance gap: are your controls watching behavior?
Explore further
27/06/2026 3:02 pm
Behavioral shadow AI governance is now more important than discovery alone: inventory tells you what exists, but not whether an identity is actively misusing access. That distinction matters because the risk sits in runtime behaviour, especially when sanctioned and unsanctioned tools can both reach sensitive data. Discovery remains useful, but it is no longer a sufficient control boundary. Practitioners should treat behaviour as the primary governance signal.
A few things that frame the scale:
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks, according to The 2024 ESG Report: Managing Non-Human Identities.
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, including 46% confirmed and 26% suspected.
A question worth separating out:
Q: Who is accountable when a sanctioned AI tool causes a data breach?
A: Accountability should sit with the owner of the identity and permissions behind the tool, not only the team that approved the application. If a sanctioned AI workflow can reach sensitive data, the organisation must govern its access path, logging, and containment as rigorously as any other high-risk identity.
👉 Read our full editorial: Shadow AI governance fails when identity behavior is invisible
