AI and Machine Identities Are Outpacing Security Teams—Why Most Organizations Are Falling Behind in Identity Management

Identity Management Was Hard, AI Made It Harder

Identity security is now a central part of cybersecurity operations, yet many organizations struggle to keep up. A recent report from SailPoint reveals that as AI-driven identities and machine accounts increase, most security teams are unprepared to manage them at scale. This gap introduces new risks and complicates the deployment of identity security across global enterprises. Notably, investments in Identity and Access Management (IAM) show the highest perceived return on investment compared to other security areas.

Most Organizations Are Still at Early Maturity Levels

The report, based on a global survey of 375 IAM leaders, shows that the majority of organizations remain in the early stages of developing mature identity programs. Sixty-three percent fall into the two lowest maturity categories, relying heavily on manual processes and basic tools for user access management. Only a small fraction have advanced to automated and adaptive identity controls.

Those advanced organizations use real-time risk data and AI to dynamically manage access, but they are the exception. Technology and financial services firms tend to be ahead, while healthcare, manufacturing, and many organizations in Europe and Latin America lag behind. Progress is uneven—three organizations advance for every two that fall back. This regression often reflects rising standards, including new demands like AI agent lifecycle management.

AI-Driven Identity Management and the Rise of Machine Identities

Identity management priorities are shifting. Traditionally, identity security focused on human users such as employees and contractors. Today, machine identities and AI agents are growing faster than any other category. These non-human identities often lack consistent governance, creating blind spots for security teams.

Less than 40% of organizations currently govern AI agents, though their numbers are expected to grow over the next three to five years. Managing these identities requires fresh strategies: just-in-time access, dynamic privilege adjustments, and continuous monitoring are becoming critical. Without these controls, machine identities risk accumulating excessive permissions or remaining active unnecessarily, opening doors for attackers.

Why Deployments Fall Short

Even with significant investment in identity security, many organizations struggle to achieve expected results. Deployment challenges are a common barrier. Only 14% of respondents reported their latest IAM deployment as completely successful. Nearly half said projects ran over budget, and 60% experienced delays of at least a month.

One major issue is application onboarding. At lower maturity levels, teams often lack full visibility of their applications and try to onboard too many simultaneously, causing gaps and errors. As organizations mature, complexity grows. Advanced organizations manage 3.6 times more applications than lower-maturity ones, each needing specific integrations and governance policies.

Data quality is another hurdle. Identity data is frequently scattered across HR systems, cloud services, and directories. Poor data hygiene weakens access controls and hampers automation. Organizations that clean and standardize identity data before rolling out new tools are far more likely to succeed.

Building for the Future

Advanced organizations are moving toward identity systems that are both adaptive and automated. AI increasingly supports these systems by handling real-time privilege adjustments, anomaly detection, and automated remediation.

To progress, organizations must first strengthen the fundamentals. Unified identity data is essential, along with structured deployment processes that prioritize critical applications and establish governance for both human and non-human identities.

• Focus on consolidating identity data from all sources to improve accuracy and control.
• Implement staged application onboarding to reduce errors and maintain oversight.
• Adopt AI-driven tools that enable dynamic access management and continuous monitoring.

Identity is the central control point where policies are enforced, decisions are made, and security operations converge. Its future lies in combining security measures with AI-driven data governance, enabling enterprises to manage every identity—human, machine, or AI agent—effectively across the organization.

For managers looking to deepen their understanding of AI’s role in security and identity management, exploring specialized AI training can provide practical skills and strategies. Consider checking out Complete AI Training’s latest AI courses for relevant learning opportunities.