Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

OpenAI Privacy Filter Isn't Enough: The Truth About AI Tokenization

While the new OpenAI privacy filter detects basic PII, true data protection requires a much deeper system. In this video, we expose the hidden security vulnerabilities inside modern AI workflows and explain why aggressive data redaction actually destroys your model's utility. What you will discover in this breakdown: The Redaction Trap: Why simply deleting sensitive data breaks your AI's contextual understanding.

Security is a core leadership issue & opportunity with David Chernitzky, Armour Cybersecurity [317]

Today David Chernitzky, Co-Founder and CEO of Armour Cybersecurity, breaks down the challenges small and mid-sized businesses face in the new blink-and-you-miss-it cybersecurity landscape. Don't be left behind and open yourself to AI-driven attacks from threat actors.

GitGuardian Just Gave AI Coding Agents Secret Detection Skills

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.

Stop Talking Tech to the Boardroom. Start Talking ROI.

The corporate firewall is dead. With cloud, remote work, and state-sponsored attacks reshaping the threat landscape, identity is now the security perimeter, and boards are paying attention to the price tag. One Identity CEO, Praerit Garg, shows CISOs how to ditch the technical jargon and make the case for identity security in the only language the boardroom understands: money, risk, and ROI.

Shadow AI Is Already In Your Company - What Can You Do About It?

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.