Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

LLM Access Controls and Audit Logging for Security Teams: A Practitioner's Guide

Most organizations have an acceptable use policy for AI tools. Very few have controls that actually enforce it. The gap between what the policy says and what security teams can detect is where insider risk lives when it comes to large language model (LLM) usage.

Your Browser Is Stealing Your Data Right Now

In this video, you will learn how lightweight OS-level instrumentation binds lineage metadata to clipboard content the moment data is copied, how that tag survives edits, reformatting, and translation across applications, and how provenance-based policy replaces pattern matching with precision rules tied to the actual source of the data. You will also learn how pairing network tools with a browser extension captures user intent before encryption, eliminating the alert fatigue that buries real risk in noise.

Why Legacy DLP Fails Against Agentic AI

Security teams that deployed legacy DLP years ago built something real. The rules fire. The alerts go out. Compliance boxes get checked. The problem is not that those programs stopped working. It is that the threat moved, and the architecture did not. Agentic AI has introduced a class of data movement that legacy DLP was never designed to govern: autonomous, continuous, multi-step, and operating at machine speed across systems that static rules cannot enumerate in advance.

How to Measure the ROI of an Insider Risk Management Program

Security leaders don't struggle to justify the need for insider risk management (IRM). They struggle to justify the budget. When the CFO or board asks why you're spending seven figures on a program to monitor your own employees, "because insider threats are real" isn't enough. Cyberhaven data shows office-based employees are 77% more likely to exfiltrate sensitive data than remote workers, and that risk spikes further during offsite logins and workforce transitions.

Cyberhaven & Torq: Bringing AI-Powered Automation to IRM and DLP

Sensitive data has become the target, the signal, and the source of risk in nearly every modern security program. Source code, customer records, intellectual property, credentials, and regulated data now move continuously across endpoints, cloud apps, SaaS platforms, browsers, collaboration tools, and GenAI applications. That movement is not inherently bad. It is how modern work gets done.

DSPM Buyer's Guide: 7 Criteria for Evaluating DSPM Tools

Most data security posture management (DSPM) evaluations start with a deceptively simple question: where does our sensitive data live? There are many tools that answer that question. However, the number of tools that go further by tracking how data moves, enforcing controls when data leaves controlled environments, and closing the gap between visibility and action are far more limited.

Your Browser Is Leaking More of Your Company's Data Than You Think

In this video, you will learn why agentic browsers like ChatGPT Atlas, Perplexity Comet, and Arc have turned the browser into a double agent inside your enterprise, how shadow adoption is bypassing MDM and endpoint controls in days, and why indirect prompt injection creates an attack surface your file-based DLP cannot see. You will also learn how data lineage replaces noisy content inspection with origin-and-destination tracking, so you can stop the leak without blocking the tools your business depends on.

Visibility Is Not Enough: The Case for Control at the Endpoint

Most security programs have more visibility than ever. Dashboards are full. Alerts are firing. And incidents are still happening. That contradiction is not a coincidence. It reflects something most security vendors have quietly avoided saying out loud: Visibility and control are not the same thing, and for a long time, the industry has been selling one while calling it the other.

Endpoint AI Agents: The New Security Blind Spot

Security teams that have invested in AI governance programs over the past two years face a problem that those programs were not designed to solve. The controls built to manage generative AI, network proxies, browser monitoring, and SSO enforcement work when data moves through defined channels. Endpoint AI agents do not move through those channels. They run locally, operate at the OS level, and access data through pathways that exist entirely outside your current visibility.