Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

5 Common AI Governance Mistakes Enterprises Make

Enterprise AI adoption has outpaced enterprise AI governance. Seventy-eight percent of organizations now use AI in at least one business function, up from 55% the year before, and most of that adoption happened before governance teams finished drafting their first policy. The result is a familiar pattern: leadership approves a rollout, security builds guardrails around the tools it knows about, and sensitive data keeps moving through channels nobody mapped.

How to Build a Red Team Exercise for AI Workflows

AI agents now retrieve data, generate recommendations, and trigger actions across enterprise systems with little human review in between. That speed is the point, and it is also the problem. A single manipulated prompt or a poisoned data source can push an AI system toward a decision no one signed off on, and most security teams have never tested for it. Building a red team exercise for AI workflows is how you find that gap before an attacker does.

Implementing AI Security: Your Enterprise LLM Security Checklist

Security teams are approving large language model (LLM) deployments faster than they can build the controls necessary to govern them and protect vital, sensitive data. Employees paste customer records into ChatGPT, engineering teams connect internal APIs to coding assistants, and business units stand up retrieval systems against production data, often without formal review.

CASB vs DLP: Key Differences and When to Use Each

Security leaders evaluating cloud access security broker (CASB) and data loss prevention (DLP) tools often discover the two categories overlap just enough to create budget friction and just little enough to leave real gaps. A CASB can flag risky file-sharing behavior in Salesforce without ever inspecting the content inside the file. A traditional DLP tool can classify that same file as containing source code without knowing whether the sharing link is public.

How to Discover and Control Shadow AI Agents in Your Environment

Most security programs have a working model for responding to shadow AI: identify the unsanctioned tools employees are using, sanction or block them, and update the acceptable use policy. That model worked, however imperfectly, when the threat was limited to web-based GenAI applications. It does not work when the threat is an autonomous agent, running locally on an endpoint, that reads the file system, calls external APIs, and transmits internal data.

How to Prevent IP Theft

Most data security programs are built around regulated data: social security numbers, payment card information, protected health information. The compliance frameworks demand it, the tooling is built for it, and breach notification laws make the stakes impossible to ignore. But intellectual property (IP) rarely triggers a regulatory deadline, which means it rarely gets the same level of protection, even though its loss can be far more damaging to a businesses bottom line, reputation, and productivity.

What Are Shadow Agents and Why Are They a Security Risk?

Most AI governance programs assume they know what they're governing. They track which AI tools employees use through browser proxies and SSO logs, block access to unauthorized platforms, and monitor data leaving through known egress channels. Shadow agents break every one of those assumptions. Agents run locally, act autonomously, and access data through pathways the tools monitoring your environment were never built to see, creating a new, and difficult to govern, attack surface.

AI Inference Risk: The Data Exposure Your DLP Can't See

Your DLP controls are correctly configured. Classification policies are in place. Sensitive data is labeled. And your AI tools are quietly building a picture of your organization that none of those controls can see. Most AI-related data exposure does not arrive as a file transfer event.

The 5 Questions Every Leak Investigation Needs to Answer

In this video, you will learn the five questions every data leak investigation must answer to be defensible — what the data is, where it originated, who accessed it, where it spread, and the fastest containment step — and why the visibility gap in most security stacks makes those questions impossible to answer instantly. You will also learn how combining DSPM baseline inventory with real-time data lineage replaces the high-stress scramble with surgical containment and audit-ready proof, so you move from "I think we're safe" to "here is the proof.".

From Paralysis to Action: Why First-Wave DSPM Left Security Teams Drowning in Data They Could Not Use

Boards are investing more in data security than ever before. Analysts have declared data security posture management (DSPM) one of the fastest-growing categories in cybersecurity. And yet CISOs across industries are standing in front of dashboards filled with findings, flags, and risk scores, completely unable to move to action.