Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI and Data Security: Why Your Data Security Model Is Hurting Innovation

Why Your Data Security Model Is Outdated For over 20 years, we’ve focused on the Data Envelope—securing the perimeter, the cloud, and the network. But in a world of AI and rapid data sharing, protecting the envelope is not enough. In this video, James Rice (VP of Product Marketing at Protegrity) explains why traditional security has become the biggest bottleneck for modern innovation. Whether you are a security leader, a data architect, or a business innovator, understanding this paradigm shift is essential for the next decade of growth.

Protecting the Language of AI: Why API Security is No Longer Optional

Protecting the Language of AI: Why API Security is No Longer Optional As AI continues to reshape the digital landscape, APIs have become the "language" of innovation—but they've also become a massive target for attackers. In this clip from the A10 Networks webinar, "APIs are the Language of AI: Protecting Them is Critical," security experts Jamison Utter and Carlo Alpuerto discuss the complexities of modern API security.

Zenity 2025 Year in Review: Building AI Security for the Enterprise

For security teams, the adoption of agents showed up operationally before it showed up strategically - creating new expectations and requirements. Risk is no longer tied to prompts or the model alone. It shows up in what agents do once they are connected to critical systems - coming from permissions they inherit, tools they invoke, and data they move.

How CrowdStrike Trains GenAI Models at Scale Using Distributed Computing

Large language models (LLMs) have revolutionized artificial intelligence and are rapidly transforming the cybersecurity landscape. As these powerful models become commonly used among both attackers and defenders, developing specialized cybersecurity LLMs has become a strategic imperative. The CrowdStrike 2025 Global Threat Report highlights a concerning trend: Threat actors are increasingly enhancing social engineering and computer network operations campaigns with LLM capabilities.

From Code to Agents: Proactively Securing AI-Native Apps with Cursor and Snyk

The rapid adoption of AI agents for development is creating a critical security gap. We are moving from predictable logic, deterministic code paths, and human-driven workflows to non-deterministic agents that reason, plan, and act autonomously using large language models across the broader software development lifecycle. As enterprises adopt these autonomous AI agents, the core challenge isn’t just the new risks and attack vectors; it’s a loss of runtime control.