Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most organizations responded to shadow AI the way they responded to shadow IT a decade ago: awareness campaigns, acceptable use policies, and training programs. The assumption was that if employees understood the risk, they would stop using unsanctioned tools. That approach did not work for shadow IT, and it won't work for shadow AI. The key difference is governance architecture.

You approved the AI tools. You funded the infrastructure. Now your teams want to deploy AI agents, and the ask sounds reasonable: automate the research workflow, connect the agent to the CRM, let it draft and send. The productivity case is clear. What is less clear is who owns the security exposure when that agent starts moving data across systems it was never explicitly authorized to touch. The answer, increasingly, is you.

Anthropic has selected Cyberhaven for its Cyber Verification Program, an application-based program that supports legitimate defensive cybersecurity work involving advanced AI capabilities. The approval gives designated Cyberhaven teams access to advanced AI capabilities with fewer interruptions from default safeguards for certain high-risk, dual-use cybersecurity tasks, subject to Anthropic's applicable policies and program requirements.

A manufacturer's most valuable assets rarely sit in a vault. They live in CAD files, chemical formulations, process parameters, supplier contracts, and tooling specifications that move every day between engineers, plants, partners, and contractors. That movement is what makes the business run, and it is also what makes trade secrets easy to lose. A departing engineer copies a design folder. A contractor forwards a spec sheet to a personal account.

SOC 2 audits are won or lost on evidence. When an auditor asks how an organization controls access to sensitive data, prevents unauthorized exfiltration, and monitors for anomalous behavior, the answer has to be documented and defensible. For most security and GRC teams, that answer depends heavily on whether their data security tooling is configured to produce audit-ready outputs, not just enforce policies.

Most insider risk programs stall at the same place: they can see what data exists, but not what users are doing with it. Data security posture management (DSPM) tools catalog sensitive files, flag misconfigured permissions, and surface overexposed repositories. What they often cannot communicate is whether that overexposed file was accessed, copied, renamed, and uploaded to a personal cloud account by an employee who put in their resignation last week.

Organizations are not choosing between AI adoption and data security. Rather, they are discovering, often after the fact, that these two priorities are pulling in opposite directions. The engineering team has been using GitHub Copilot for six months. Finance is running variance analysis through ChatGPT. Legal is pasting contract language into Gemini for redlining. According to Cyberhaven Labs research, 39.7% of the data employees share with AI tools is sensitive.

Clinicians are pasting patient summaries into ChatGPT to draft discharge instructions. Billing staff are uploading claim data to AI writing tools to speed up appeals letters. Nurses are using consumer AI assistants to look up drug interactions between patient visits. None of this was approved by the security team, and most of it would surprise the compliance officer.

AI agents are connecting to enterprise systems right now. Whether a developer wired up Claude to an internal Confluence instance, a vendor shipped an agentic workflow that calls the CRM, or an employee enabled a browser-based AI assistant that reads email, Model Context Protocol (MCP) is rapidly becoming the integration layer between large language models (LLMs) and corporate data. Most security teams have no visibility into any of it.

HR teams manage every stage of the employee lifecycle, from hiring and onboarding to performance management and offboarding. Security teams manage data access, behavioral monitoring, and incident response. Insider risk lives at the intersection of both. When HR and security operate independently, the gaps between them are exactly where data loss happens, and the moments of highest exposure are almost always HR events, such as a resignation submitted, a role change processed, a termination decision made.