Confronting Emergent Risks in the Everything-Everywhere AI Ecosystem
Generative AI is fundamentally reshaping cybersecurity. With the rise of large language models (LLMs) and autonomous agents, traditional security boundaries like endpoint, network, cloud, and data, no longer contain security risks as they once did. AI-driven workflows freely cross these boundaries, illustrating how ubiquitous AI has become in the enterprise, and it’s eroding the old approach to cybersecurity.
This paper examines why AI marks a true breakpoint for cybersecurity. It details how existing tools fail to capture AI-driven behavior, what new forms of risk emerge when users, agents, and applications operate together, and how organizations must evolve their strategies to keep pace. It also offers a framework for evaluating vendor claims and identifying which technologies are genuinely equipped for the AI era.
Readers Will Learn:
- Why AI breaks traditional security perimeters and creates risk that spans endpoints, networks, cloud, and SaaS simultaneously.
- How emergent risk forms when prompts, agents, and data flows interact—often appearing harmless in isolation but dangerous in combination.
- Where legacy vendors fail, and how existing EDR, CASB, DLP, and IAM tools provide only fragmented visibility.
- What unified runtime context means, and why continuous observation of users, agents, and applications is now essential.
- How a platform approach enables governance in motion, applying real-time policy, adaptive risk scoring, and auditability across the entire AI lifecycle.