Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Sensitive data used to live in predictable places database columns, known field names, structured rows. That changed when data moved into documents. And it changed again when AI workflows arrived. In this video, we walk through why detecting sensitive data in AI pipelines is fundamentally different from traditional data discovery, and why the old approaches break. We cover the four failure modes that make detection hard in AI workflows.

Shadow AI is already on your employees' phones — and it's invisible to your network controls. This demo follows a real workflow: an employee gets blocked from using an unauthorized AI tool on her corporate laptop, so she switches to her personal phone instead. No VPN, no DLP, no visibility, no corporate controls follow her there.

In this week's Intel Chat, Chris Luft and Matt Bromiley discuss how the same AI capabilities fueling adversaries are available to defenders too. Matt's takeaway: you don't need to buy an AI product to keep pace. The same way an attacker points AI at a code base, defenders can point it at detection rules and telemetry. Chris adds that as more developers use these models to check their own code, the playing field will level out, though the next year or two will likely bring a spike in exploits from lower-skilled attackers leveraging AI before defenses catch up.

AI Is Accelerating the Cyber Kill Chain: Faster Attacks, Greater Risk AI is changing the speed and scale of modern cyber attacks. From accelerating reconnaissance to reducing the time required for initial access, attackers are leveraging AI to move faster across the entire cyber kill chain. In this video, Paul Girardi explains how AI is impacting each stage of the attack lifecycle, including: As attackers automate more of the kill chain, security teams need smarter approaches to detect, disrupt, and deceive adversaries before they can achieve their objectives.