Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Champion / Spokesperson(s): Brendan Putek, Director of DevOps, and Esaie Batoula, Security Engineer. Relay Network is the innovator behind a secure B2C communications platform that combines SMS with dynamic feed technology to help regulated enterprises deliver personalized, action-oriented mobile experiences for every customer. In an industry where trust, compliance, and data protection are paramount, security has always been central to how the company builds software.

Companies and employees are racing to capture the value and efficiencies offered by AI, but security is often an afterthought. Employees are using unauthorized GenAI tools to summarize documents, draft emails, and analyze potentially sensitive or proprietary data. Developers are adding AI capabilities before security teams can review them. SaaS platforms are adding AI features that may process sensitive business data by default.

AI agents are connecting to enterprise systems right now. Whether a developer wired up Claude to an internal Confluence instance, a vendor shipped an agentic workflow that calls the CRM, or an employee enabled a browser-based AI assistant that reads email, Model Context Protocol (MCP) is rapidly becoming the integration layer between large language models (LLMs) and corporate data. Most security teams have no visibility into any of it.

AI usage control is the security and governance framework that enterprises use to monitor, regulate, and secure how employees interact with artificial intelligence tools. As Generative AI becomes deeply embedded in everyday workflows, organizations face a high-stakes balancing act: capturing massive productivity gains while preventing catastrophic data leaks, compliance violations, and intellectual property exposure.

If AI agents are becoming organizational actors, then governance needs to move beyond principles and into operational structure. In Camille Stewart Gloster’s upcoming book The Insider You Build, she explains that governance is not defined by policies or structures, but by whether it can actually influence system behavior at runtime. In an agentic environment, governance only exists where it can shape, constrain, and intervene in decisions as they happen.

Cosine similarity is pure math. No magic. No understanding. Once you accept that, a lot of the confusion goes away. We talk to a lot of customers, and even seasoned engineers, who treat cosine similarity like magic that solves everything. Engineers talk about embeddings like they are definitive. Product teams trust similarity scores like they are facts. Vendors sell “semantic understanding” like the model actually understands. Truth is, it does not.

As large language models (LLMs) become more embedded in business operations, the risks and attack methods targeting them are evolving just as quickly. The 2025 edition of the OWASP Top 10 for LLM Applications reflects this rapid evolution, addressing the current threats facing generative AI systems in production environments. For organizations investing in LLMs, understanding the risks is crucial for deploying these systems securely.

Two protocols are shaping the AI revolution: A2A for agent-to-agent delegation, and MCP for agent access to tools and external systems. A2A expands who can participate in a workflow by enabling agent-to-agent delegation. MCP expands what agents can reach by connecting them to data and systems. By the end of 2026, task-specific AI agents are expected to appear in 40% of enterprise applications, up from less than 5% in 2025. That shift changes where security has to live.