aiqbench analysis
This is a vendor announcement for Protegrity AI Team Edition, a policy engine meant to govern data and AI access across people, agents, and queries. The survey figures it cites (EMA's "AI friction" research) are self-reported and come from a vendor-sponsored framing, so treat the claims about enterprise delays and limits as directional rather than established. For readers, the substance is the architectural idea of enforcing one policy layer at the data, agent, and inference stages, which is worth evaluating against your own access-control and agent-permissioning needs.
AI-ready by design: all the context AI needs, none of the risk
See how AI agents are changing zero-day vulnerability discovery, cyberattack economics, and incident response—and why security teams need continuous assessment, faster remediation, and locally hosted AI capabilities. The post AI-ready by design: all the context AI needs, none of the risk appeared first on Protegrity.
Centrally control what thousands of agents and queries can access, with a new policy engine and protection enforced at the data, agent and inference layers. Every AI project runs on context: the data, documents and company knowledge that people and agents draw on to make decisions and take action. And the context that makes AI most useful is usually the most sensitive, from customer records and health information to financial data and contracts. That leaves most enterprises with a trade-off. They can give AI enough context to be useful and accept the exposure, or hold sensitive data back and delay or diminish their AI initiatives. EMA’s State of AI Friction research found that 83% of organizations have had AI projects delayed by security or compliance reviews, 82% have shipped AI with…
Excerpt shown under the publisher's RSS terms · 131 words · full text and figures are on the publisher's site