Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

What an AI Correlation Rule Cannot See

A correlation rule can be tuned. The window can be widened, the join key improved, a source promoted from optional to required. Each of those is a parameter with a defensible setting. ‍ What remains after all of it is the residual, meaning the events that would produce a finding if a source existed for them, which is a form of residual risk expressed in detection terms. Naming it is the question an auditor asks after being shown a detection, and the answer is not a tuning exercise. ‍

Govern the AI agent as the identity it is

AI agents are non-human identities. They hold credentials, carry permissions, and act on systems around the clock, usually with standing access nobody reviews. The Salesloft Drift breach reached more than 700 organizations through exactly that kind of over-scoped, non-expiring token, with no prompt injection involved. The controls already exist: inventory every agent, scope it to least privilege, expire its access, review it on a schedule, and detect when it's abused.

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Governing at the speed of AI: What IT leaders need to know

AI is reshaping the IT leadership role faster than any shift in the last decade. As AI makes it dramatically easier to build software (agents, apps, automations, and more), the volume of software entering the enterprise is far beyond what IT teams have had to govern before. That shift demands new ways of thinking about enterprise control: how security, access, and oversight evolve to keep pace with software that's built faster and by far more people than ever before. The leaders who treat this as an operating shift, not just a technical one, are the ones expanding their influence.

Securing Your AI Agents: Native AI Detection and Response Across the Full Agentic Path

Enterprises are deploying AI agents at scale, and those agents are taking real business actions. Through LLMs, MCP servers, and APIs, they move money, access patient records, modify code, and send emails. The attack surface has fundamentally shifted, but most security programs are still focused on what an AI says rather than what it does.

AI Governance Where the Regulator Also Runs the Market

AI governance evidence is usually prepared for a neutral reader. A regulator with no stake in the market, an auditor with no competing product, an examiner who gains nothing from what the documentation contains. ‍ In securities and derivatives markets that assumption does not hold. Exchanges and clearing organizations register as self-regulatory organizations, and most of them operate the market while regulating its participants. The reader of your evidence is also an operator. ‍