Bottom line: AI-driven systems automatically detect unused permissions in cloud applications and shadow IT, while more than 80 percent of all data breaches stem from overprivileged accounts.
Manual rights management in modern IT environments leads to systematic over-privileging, as employees accumulate unused permissions over their tenure. Machine learning now automates the detection of these security gaps even in unused shadow systems.
The management of digital access rights has undergone fundamental change in recent years. While in the past employees moved through fixed departments and used central systems, today agile project teams, frequent departmental changes, external service providers and numerous cloud applications characterize everyday work. In this environment, manual identity management almost inevitably leads to employees accumulating permissions they do not need for their current position.
The core problem is structural: IT is typically only involved during onboarding of new employees. When projects change or departments change, old rights are forgotten because day-to-day operations take precedence. A particular complexity arises from shadow IT: business units often purchase specialized cloud software such as design platforms, analytics tools or agile board systems in an uncoordinated manner without integrating them into central identity management. These applications generate locally bound permissions that are not captured by central control mechanisms.
The security implications are considerable. The Verizon Data Breach Investigations Report and long-term studies by the Ponemon Institute demonstrate that over eighty percent of documented data breaches stem from legitimate, overprivileged user accounts. When an employee’s cloud account is compromised, the scope of their permissions determines the damage radius: an account with undetected administrative rights to a decentralized customer database allows attackers to exfiltrate sensitive data without triggering alerts from central security monitoring.
Machine learning addresses this problem through automated detection of unused permissions in decentralized systems. AI-driven identity governance solutions aggregate access logs from various cloud providers, identify unused privileges and suggest reductions. This enables continuous removal of overprivileging rather than one-time assignment. The Gartner Market Guide for Identity Governance and Administration attests to this technology’s growing importance as a necessary response to the complexity of modern identity ecosystems.
Source: www.it-daily.net · Published July 18, 2026
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