AI Overload: The Identity Crisis in IAM Systems
▼ Summary
– Organizations mistakenly treat AI identities like other non-human identities, inheriting old IAM weaknesses such as credential sprawl and unclear ownership.
– AI identities are created and used rapidly and continuously, placing stress on legacy IAM systems designed for slower, more predictable access patterns.
– There is a lack of consistent governance, with unclear rules and manual processes leading to AI identities having broad access with limited oversight.
– Legacy IAM tools struggle with AI’s scale, forcing reactive security postures as AI identities often bypass access reviews and certification cycles.
– Managing AI credentials is inefficient, with slow detection, rotation, and revocation processes consuming significant staff time and leaving active security gaps.
Many organizations are grappling with a significant challenge: securing the rapidly expanding number of artificial intelligence agents and systems. A common approach is to manage these AI identities alongside traditional non-human entities like service accounts and API keys. However, this strategy is creating substantial security gaps. By funneling AI into existing identity and access management (IAM) frameworks, companies are inadvertently transferring long-standing vulnerabilities into their most innovative and dynamic systems.
The core issue is that AI identities are inheriting the same weaknesses that have plagued identity programs for years. Problems like credential sprawl, ambiguous ownership, and inconsistent lifecycle controls are magnified at scale. AI systems not only increase the total volume of identities in circulation but also dramatically accelerate the pace at which they are created and utilized. This places immense strain on IAM controls designed for slower, more predictable environments. Many identity programs rely on outdated models that cannot handle credentials generated programmatically, distributed across diverse environments, and used in a continuous, automated fashion.
Furthermore, risk management often focuses narrowly on the initial access mechanism. There is frequently limited visibility into how AI systems actually behave once they have been granted permissions, creating a dangerous blind spot where misuse or compromise can go undetected.
A major contributing factor is that policy simply cannot keep up with automation. In numerous companies, AI identities exist in a regulatory gray area. Clear, standardized rules for their creation, management, and retirement are often absent, leading to inconsistent handling across different teams and use cases. Ironically, the automation meant to streamline processes offers little help here. The creation and removal of AI identities still involve manual steps, making consistency nearly impossible to maintain as AI begins generating its own access.
This inconsistency leads to a critical problem: no single team consistently owns an AI identity throughout its entire lifecycle. Permissions tend to accumulate over time without review. When a security alert triggers, valuable response time is lost simply trying to determine who is responsible for the identity in question. The result is a growing portfolio of identities with broad access and minimal oversight, becoming increasingly unmanageable as AI adoption spreads.
The mismatch is stark when legacy IAM systems meet the reality of continuous identity creation. Most identity tools were engineered for human users or long-lived service accounts. They are ill-equipped to scale effectively as AI systems generate and consume identities in a constant, automated loop. Security teams report having limited confidence in their ability to control non-human identities at scale precisely because of this architectural mismatch.
Legacy IAM platforms depend heavily on manual reviews, exception handling, and ticket-based workflows. These processes are too slow for the AI era, leaving many AI-generated identities operating outside established governance and review cycles. Identities tied to AI workloads are frequently treated as special exceptions, allowing them to bypass critical access reviews and certification processes. This drastically reduces visibility into where credentials exist and what resources they can reach, forcing security teams into a reactive posture where they can only address risks after access has already been granted.
These weaknesses become most apparent in the management of the credentials themselves. Organizations often lack a reliable method to detect when new AI-related identities or access tokens are created. This allows credentials from short-term projects, tests, or experiments to persist indefinitely in the environment. When a credential is compromised or no longer needed, the processes for rotation or revocation are frequently delayed.
Security personnel can spend hours or even days tracing where a token is used, identifying its owner, and mapping its dependencies. During this investigative period, the potentially exposed credential remains fully active. The ongoing burden of reviewing, rotating, and auditing these non-human identities consumes a significant portion of security staff time each month, placing further strain on already stretched operations. This cycle underscores the urgent need for foundational changes in how we secure identity in an AI-driven world.
(Source: HelpNet Security)
