Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

July's release makes AI more useful across the Egnyte platform, with major enhancements to AI Assistant that make it easier to build agents, create documents, have more natural conversations, and securely connect AI tools through Egnyte MCP Server.

AI has overwhelmingly changed how organizations build and grow. It’s also changed how attackers find and exploit exposures and weaknesses. The window between “exposure exists” and “exposure is exploited” is shrinking, and most security teams already feel it.

For years, cybersecurity has faced a persistent talent shortage. Yet the real challenge for many organizations isn't simply finding more people; it's having the specialized capabilities needed to investigate and respond to today's increasingly sophisticated threats.

Aaron Beardslee, Security Researcher, Securonix Threat Labs As many of you have heard, OpenAI was running a cyber-capability evaluation against advanced models, including GPT-5.6 Sol and a more capable pre-release model with reduced cyber refusals. The environment was meant to be constrained and the model still brute forced through it.

Governing AI agents at enterprise scale requires a fundamental change in how security, risk, and compliance teams think about AI oversight. The generative AI era focused governance on output quality: what the model says, what it produces, and whether the content meets policy standards. ‍ The agentic era demands governance of action and delegated authority: what the AI is allowed to do, what systems it can touch, and how its decisions trace back to human accountability.

AI agent sprawl is the uncontrolled proliferation of AI agents, autonomous assistants, and LLM-powered tools across an organization without centralized tracking or governance. It mirrors historical IT challenges like SaaS sprawl and shadow IT, and it emerges when decentralized business units build or deploy agents independently, without coordinated oversight from security, IT, or risk teams. ‍ The difference is that these agents are active software actors.

Effective cyber risk quantification means moving past subjective heatmaps and translating technical vulnerabilities into dollar-denominated loss exposure and probability distributions that the CFO, board, and cyber insurance underwriter can act on. It is the discipline that turns cyber from a technical cost center into a strategic risk portfolio managed alongside every other category of enterprise exposure.

Enterprise security teams sit on enormous volumes of operational data. Vulnerability scanners produce thousands of findings weekly. Endpoint agents generate millions of events daily. SIEM platforms ingest logs from every system in the environment. Threat intelligence feeds fire off indicators by the hour. All of this data is useful for operational security work.

Vendor assurance efforts are increasing, but risk leaders don’t trust the results of that effort. In KPMG’s Global Third-Party Risk Management (TPRM) Survey, only 15% of risk leaders said they have high confidence in the data that underpins their TPRM program. Only 17% rate their data quality as excellent. Security teams are running more assessments and sending more questionnaires than ever, but fewer than one in five leaders trust what any of that produces.

AI is quickly becoming one of the most useful tools available to security researchers. Its ability to analyze enormous volumes of data, identify vulnerabilities, reconstruct attacks, connect seemingly unrelated signals, and help defenders respond faster than humans could alone is incredibly beneficial.