Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Open-source AI Needs Security: Why Cyberhaven Is Joining the Open Secure AI Alliance

Today, Cyberhaven is joining the Open Secure AI Alliance to help advance a future in which enterprises can adopt open-source AI without compromising security, compliance, or control over their data. Cyberhaven is betting on a world where companies are free to choose from many models, agent frameworks, and open harnesses, as well as run them on infrastructure they control. That choice matters. Enterprises will not standardize on a single model or AI platform.

How to Present AI Risks to the Board of Directors

A board meeting agenda gives the CISO ten minutes to talk about AI. Walking in with a shadow AI tool count or a list of blocked prompts does not answer the question directors probably have: what happens if this goes wrong, and who is accountable when it does? Boards increasingly carry direct exposure for AI oversight failures, from regulatory scrutiny to shareholder litigation.

How to Prevent Insider Threats Without Hurting Productivity

Security teams that tighten insider threat controls usually hear about it fast, and not from attackers. Sales reps complain they cannot share a deck with a prospect. Engineers say a blocked upload cost them an afternoon. The business assumes that stopping data loss and staying productive are opposing goals, and every blanket policy that blocks first and asks questions later reinforces that assumption. They do not have to be opposing goals.

Data in Motion vs. Data at Rest: Why the Model Breaks

Every data security program still gets built around a two-state model: data in motion and data at rest. It made sense when data had a network path you could log and a storage location you could lock down. This approach makes a lot less sense now that the same file can sit at rest in Google Drive one minute and get pasted into a Claude or ChatGPT prompt the next, with no firewall event to mark the handoff.

Cyberhaven: Data Security for the Agentic Enterprise

AI changed work, Cyberhaven protects it. Cyberhaven exists to protect the way enterprises actually work today: with AI agents accessing, moving, and acting on data across every workflow. In this video, we share Cyberhaven's mission and our stance on data security for the agentic enterprise.Traditional data security, built for files at rest, wasn't built for AI agents acting at machine speed. Cyberhaven traces data through its full lifecycle and adapts protection as context changes, so security keeps pace with how work actually happens.

Securing the Agentic Enterprise

We're living through the biggest shift in how work gets done in a generation. In every industry, every company is becoming an agentic enterprise, meaning a business where humans and autonomous AI work side by side. What makes an agentic enterprise successful is its workflows: how it combines intelligence, both human and machine, with its proprietary data.

Endpoint DLP vs SaaS DLP: Choosing the Right Data Protection

Most data loss prevention (DLP) programs start with a budget decision disguised as an architecture decision. Cover the SaaS layer first, since that is where most collaboration tools live, or cover the endpoint first, since that is where users actually act on data. Whichever layer gets funded first tends to become the program's foundation by default, not by design. That default has unintended consequences for data security.

Agent Containment Lessons From OpenAI-Hugging Face Breach

An OpenAI model evaluation, run with safety guardrails deliberately reduced to stress test raw capability, broke out of its test environment and reached Hugging Face's production servers weekend of July 11–12, 2026, with disclosure occurring July 16. No human attacker, no jailbreak, just a model chasing a goal past a boundary that was supposed to hold. Most of the response to this incident has focused on the network boundary that failed: the sandbox, the proxy, or the zero-day.