Cybersecurity programs are under pressure from two directions at once: enterprise environments are becoming more dynamic and distributed, while threat actors are becoming faster, more specialized, and more selective in how they target organizations. Traditional, tool-centric security models struggle in this environment because they generate large volumes of findings without consistently showing which assets, exposures, and defensive gaps matter most to the business

Agentic autonomy is both a capability and an attack surface. This guide covers how agentic AI works, where the vulnerabilities live, and how to build architectures that won't hand threat actors the keys.

Agentic AI systems introduce new vulnerabilities and exposure paths that require continuous discovery, prioritization, and remediation. In this video, we explore how organizations can identify weaknesses across agents, trust relationships, software components, and network interactions to improve overall security posture. Learn how risks such as prompt injection, exposed secrets, authentication tokens, vulnerable agent code, and inappropriate trust between agents can be identified and prioritized—and how remediation may involve new controls, restricting agent scope, or replacing or terminating agents. Securing agentic AI means continuously understanding where exposures exist, which ones matter most, and taking action to reduce risk.

Agentic AI systems create complex traffic flows across users, agents, LLMs, MCP servers, gateways, APIs, and enterprise data. In this video, we explore why end-to-end monitoring and visibility are essential for understanding what happens after a user submits a prompt. Learn how organizations can monitor agentic traffic flows, agent-to-API interactions, user activity, agent behavior, data access, anomalies, and looping activity—and how centralized AI gateways can help connect these signals across the full agentic lifecycle. Effective AI security requires visibility into who initiated an action, which agent handled it, what data was accessed, and how that activity flowed through the environment.

What happens when an AI agent keeps trying to satisfy a user request—even when it should not have access to the data it needs? In this video, we examine a rogue agent scenario in which an overly permissive agent repeatedly attempts to access sensitive enterprise information across multiple data sources. Learn how AI gateways, behavioral monitoring, anomaly detection, traffic visibility, guardrails, and access controls can help identify rogue agent behavior, block unauthorized access, and terminate risky activity before sensitive data is exposed. Securing agentic AI requires continuous visibility into what agents are doing, what they are trying to access, and when their behavior moves outside expected boundaries.

Agentic AI introduces a new layer of non-human identities that must be governed as carefully as human users. In this video, we explore five critical controls every agent should pass before accessing enterprise data—from verifying agent identity and trust to authorization, granular permissions, and ongoing access governance. Learn how organizations can apply least-privilege access to agents, personas, and “mini-me” agents; tie delegated access back to users; restrict agents to only the data required for their tasks; and continuously adjust or terminate permissions as responsibilities change. Effective AI security requires knowing not only who the agent is, but also what it is authorized to access and for how long.

Agentic AI introduces a new enterprise asset estate that traditional inventories may not fully capture. In this video, we explore how security teams can identify and inventory agentic assets—including agents, sub-agents, personas, LLMs, MCP servers, APIs, service identities, gateways, and the data they access. Learn why agentic asset discovery is foundational to securing AI systems, how these assets can be distributed across cloud, on-premises, SaaS, and developer environments, and how approaches such as AI gateways, MCP traffic, SASE platforms, and web logs can help build a more complete AI asset registry. A well-managed agentic AI estate starts with knowing what exists, where it resides, and how it connects.

The first half of 2026 saw attackers increasingly target trusted enterprise technologies rather than traditional endpoints. Cloud identities, software supply chains, SaaS platforms, AI infrastructure, and enterprise management systems became preferred paths to achieve broad access.