Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As organizations scale ChatGPT Enterprise across departments, AI is becoming embedded in everyday business operations. Finance teams are building custom GPTs. Developers are leveraging Codex to act on codebases. Employees are invoking third-party tools within AI conversations to automate workflows. As adoption accelerates, security teams face a fundamental challenge: visibility around agents deployed and running in SaaS environments.

Understand where short-lived credentials reduce risk in agentic systems and where operational complexity requires stronger monitoring and governance controls.

Last week, OpenAI released Privacy Filter, an open-weight model for detecting and redacting PII in text. It is a thoughtful release: Apache 2.0 licensed, able to run locally, designed for high-throughput workflows, and built to go beyond regex-based detection. This is good news for everyone building enterprise AI. Privacy at the model layer is getting real attention. What we liked most was how clearly OpenAI described the role of the model.

AI agents create data exfiltration risk by combining three capabilities that are dangerous together: access to private data, exposure to untrusted content, and the ability to communicate externally. When all three exist in one agent, an attacker can hide instructions inside an email, document, or webpage the agent processes and trick it into sending sensitive data out. No software vulnerability is required. The attacker doesn't need to break in. They just need to talk to your agent.