News / Cybersecurity
The Limits of Classic IAM Systems in Managing AI Agents: A Practical Framework for Businesses Published on 29 September 2026 by Christ-loisele (3 min read)
Published by The Hacker News on September 28, 2026, an article reveals that traditional IAM architectures fail to control autonomous agents. These systems do not track their actions or dynamically limit their permissions, posing a major security risk for businesses.
Why are traditional IAM systems unsuitable for AI agents?
Platforms designed for Identity and Access Management (IAM) targeting human users or static services cannot track the behavior of AI agents, according to The Hacker News . These systems fail to document what an autonomous agent has done after gaining access, thereby creating an identity dark zone (« identity dark matter ») : accounts, tokens, or permissions that are not logged in central governance systems.
For example, an AI agent may accumulate secrets such as static API keys or tokens without rotation, even after completing its task. Worse, these agents often inherit overly broad user or service permissions, without restrictions tied to their specific objective. Result : increased exposure to abuse or data leaks, as highlighted in the article by citing the NIST SP 800-53 Rev. 5 principle of least privilege.
Without adapted controls, an AI agent with broad permissions will inevitably exercise capabilities beyond its approved mission, according to recommendations from OWASP in its Top 10 for applications based on large language models.
Illustration: Lawing Tech The autonomy of AI agents makes static permissions obsolete
AI agents dynamically chain tasks and select tools in real time, a flexibility that is incompatible with the static permissions of classic IAM systems, The Hacker News notes. The latter assign permanent roles, whereas agents require short-lived authorizations (« task-scoped grants »), expiring once the task is completed. The article cites OWASP in its Top 10 for Large Language Model Applications (LLM06): an agent granted broad permissions will inevitably exceed its approved scope.
Moreover, agent identities are often created by deployment pipelines or technical teams, rather than through processes managed by human resources. This results in untraceable identities, instantiated by other workloads without being recorded in the identity provider (IdP ) or governance systems.
What this means here: risks and adaptations for Beninese and West African businesses
For businesses and administrations in Benin and West Africa, the growing adoption of AI agents in sectors such as finance, healthcare, or logistics could exacerbate vulnerabilities if current IAM systems are not revised. For example, an agent automating banking transactions or medical diagnostics could, without fine-grained control, access sensitive data beyond its original purpose, as described in the IAM for AI Agents framework published by The Hacker News .
Local organizations should assess their cybersecurity maturity to integrate mechanisms such as:
assigning a distinct and identifiable identity to each agent, avoiding shared accounts or borrowed human credentials, applying the principle of least privilege and allowlisting (explicit authorization of APIs and functions used), implementing task-scoped grants , where permissions expire with the task, rather than permanently. Without these adjustments, risks of identity dark matter , unmonitored agents or secrets, could undermine regulatory compliance (such as BCEAO requirements for the financial sector) and increase attack surfaces , particularly through non-rotating API keys or inappropriate inherited permissions.
Toward dynamic governance: observability and machine-readable policies
The article emphasizes that IAM frameworks for AI agents must include real-time observability , as also noted by CyberWebSpider citing The Hacker News . This involves monitoring agent actions, detecting deviations from their original tasks, and ensuring clear accountability. Companies may need to extend their existing IAM platforms, develop custom solutions, or adopt specialized tools for traceability.
In the long term, effective governance will rely on machine-readable policies and continuous authorization , enabling dynamic adaptation to agent behaviors. For Beninese stakeholders, this could involve partnerships with IAM solution providers or regional cybersecurity labs, capable of offering frameworks tailored to local constraints (such as hybrid infrastructures or limited connectivity).
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