Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI agents are autonomous, powerful, and deeply embedded in how modern businesses operate. From rerouting customer support emails to accessing critical business tools like email and CRM systems, agents are transforming workflows across departments. As of Microsoft’s Q1 2025 earnings report, over 230,000 organizations, including 90% of the Fortune 500, are using Microsoft Copilot Studio to build custom agents for a huge variety of tasks.

As enterprises embrace AI, identity has become the defining security challenge. Every new database, Kubernetes cluster, SaaS app, and now every AI agent introduces yet another identity that must be governed and protected. At the same time, attackers are weaponizing AI to accelerate identity-based threats, exploiting fragmentation and credential sprawl to devastating effect.

As Anna say in the podcast, “Security reviews show up just when you think the deal is about to close. It’s like a final boss that no one wants to fight.” The last-mile friction caused by security diligence isn’t new, but it’s becoming more painful as deal cycles tighten and expectations around transparency rise. Buyers want answers faster. Vendors want to close faster. And security teams, stuck in the middle, are often left juggling risk, reputation, and revenue timelines.

The state of cybersecurity has always been in flux, but the arrival of tools like ChatGPT heralded one of the most significant challenges for security teams in years. AI has the potential to unlock incredible potential in data processing and malware detection, but in the wrong hands, Large Language Models (LLMs) and other adversarial AI tools can be used to develop polymorphic malware that can escape detection, gain access to sensitive data, and poison data sets.