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Organizations managing massive volumes of sensitive data across cloud, SaaS, hybrid, and AI environments increasingly rely on data security posture management (DSPM) platforms to reduce exposure risk, improve visibility, and support AI adoption.

Cyera has emerg...

Agentic AI governance differs from traditional AI governance in four fundamental ways: autonomous decision oversight, system access control, workflow execution monitoring, and data exposure risk. 

Traditional governance was built for models that respond to prompts.

Agentic AI doesn’t wait for prompts.

It pursues goals, sel...

AI regulations are increasingly shaping how organizations govern agentic AI—systems that don’t just generate outputs, but take actions. Across major frameworks, a consistent set of expectations is emerging: transparency, auditability, data minimization, and meaningful human oversight.

These expectations are becoming foundational to modern agentic AI go...

Most agentic AI governance platforms are solving the wrong problem. They watch what agents say and do at the surface level.

However, the real exposure lies one layer deeper: in the sensitive data that agents touch, the permissions they accumulate, and the identities that no one has connected to any of it. 

If you’re evaluating AI agent governance tools, tha...

When a regulator asks why your AI system denied a loan, flagged a transaction, or recommended a personnel action, “the model decided” is not an answer.

CISOs, chief privacy officers, and data governance leaders are increasingly realizing that AI explainability is less a model problem and more a data governance problem. It requires visibility into the d...