Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The attack lifecycle is compressing. Frontier AI models like Anthropic’s Mythos and OpenAI’s GPT-5.5-Cyber can help bad actors research vulnerabilities, test approaches, adapt code, and change delivery methods at machine speed and scale. That reduces the time, skill, and coordination needed to move from vulnerability discovery to active attack. When attacks behave this way, security needs to operate in real time with full visibility and context across the attack path.

In this video, you will learn why static domain-blocking strategies fail against the modern Shadow AI ecosystem, how Generative AI wrappers, browser extensions, and personal accounts bypass corporate firewalls without triggering an alert, and why network-layer inspection cannot distinguish proprietary code from public Stack Overflow snippets. We break down the limitations of traditional DLP at the clipboard layer, explain how data lineage replaces application allow-lists, and show how the "Glass House" model lets enterprises enable AI productivity while strictly gating sensitive data movement.

AI coding assistants like Claude Code and Cursor are helping developers write more code faster, but that also means more chances for secrets to slip into prompts, files, commits, and tool outputs. GitGuardian’s new open-source **agent-skills** repository teaches AI agents how to use **ggshield** directly inside the developer workflow: when to scan, how to read findings, and how to guide remediation for leaked credentials.

Read the full report here: https://www.gitguardian.com/state-of-secrets-sprawl-report-2026