Most enterprises did not design their access environments for AI. They designed them over years of employees joining teams, changing roles, sharing folders, adding applications, creating service accounts, …
AI agents have introduced a new identity problem into the enterprise. They can authenticate through applications, service accounts, machine identities, API credentials, OAuth grants, cloud roles, or delegated …
An AI agent’s risk is not determined by the model alone. Security teams need to understand what sits around the model: the data it can reach, the identities …
Two AI agents can use the same model, run the same version, receive the same prompt, and even operate with the same security controls around the model. Yet …
Ask a security team to map an application attack surface and the exercise feels familiar. They identify endpoints, APIs, identities, vulnerabilities, integrations, and security boundaries. AI changes that …
Most organizations measure AI security after something happens. A sensitive prompt triggers an alert. An employee pastes confidential data into an AI application. A copilot returns information someone …
AI does not need to leak sensitive data to create a data-security problem. Sometimes the problem starts much earlier. A copilot can search files that an employee technically …
Threat intelligence has always had a speed problem. Attackers move quickly. Security teams collect enormous volumes of indicators, vulnerabilities, telemetry, research, alerts, and adversary behavior. Analysts then have …
AI agents create value because they can access data and act on it. They can retrieve customer records, search enterprise documents, query databases, call APIs, send messages, update …
AI agents do not work alone anymore. One agent researches. Another retrieves enterprise data. Another evaluates the information. Another calls an API. Another changes a record or triggers …