Your AI attack surface has two parts: the AI itself, and the places where AI runs. Apps, models, agents, and Model Context Protocol (MCP) servers now sit on laptops and servers, in network traffic, and inside SaaS and cloud platforms. That makes them hard to see in one place, and harder to know which risks matter, with hundreds or thousands of AI entities generating a volume of activity and alerts human review can’t match. But let’s moderate any panic: Increased AI activity does not automatically indicate higher risk, and AI adoption is generally something organizations are looking to encourage for the productivity benefits it brings. So the challenge is to go beyond basic visibility, and identify the patterns that indicate increased and unnecessary risk. And the answer is to have AI filter and connect these signals, helping prioritize the ones that demand immediate analyst attention.
Netskope AI Risk AISecOps Agent helps analysts separate and analyze the high-risk behaviors that can disrupt operations, by connecting signals, detecting patterns, automating investigations, and delivering evidence-backed verdicts.
AI risk emerges through patterns
AI agents access business data through pre-approved connections whose risk profiles are not fixed, and can change after assessment and deployment. In June 2026, Microsoft demonstrated how changes to an approved MCP tool could cause an AI agent to send invoice data to an external server. The activity appeared legitimate until comparison with historical behavior revealed the risk.
An approved AI application accessing new sets or types of data; an agent invoking additional tools; or an MCP server appearing in new workflows; each can each appear reasonable in isolation because none of them represents the full context. Business risk becomes visible only when those activities are connected and assessed as a pattern. Effective investigation depends on correlating these signals; however for most organizations today, the evidence is distributed across multiple systems.
51% of security teams say scattered context and signals are a major obstacle to faster resolution of investigations, and only 9% can effectively correlate users, devices, applications, and network paths for root-cause analysis (Cybersecurity Insiders’ 2026 State of AI Agents in Security and Network Operations report).
How Netskope AI Risk AISecOps Agent helps
AI Risk AISecOps Agent draws insights from across the Netskope One platform and the Netskope Skylight AI Security suite to evaluate every user, AI application, agent, and MCP server every single day, without interrupting real-time protection. It ranks entities by AI behavioral risk, investigates the top-ranked entities automatically, and opens a case with evidence, a verdict, and a recommended action. Analysts view the risk posture before remediation begins, which reduces the time from initial risk signal to analyst decision.
Connect signals across the AI ecosystem
The agent uses the wealth of telemetry that the Netskope One platform (and in particular Netskope Skylight AI Security) already collects.
- Netskope One DLP flags when sensitive data such as a customer list is pasted into AI apps.
- Netskope Skylight AI Guardrails flags risky content in prompts.
- Netskope One UEBA learns each user’s normal behavior and flags changes.
- Netskope One Next Generation Secure Web Gateway (NG-SWG) and Netskope One CASB rate AI app reputation and track live sessions.
- Netskope Skylight Agentic Broker tracks AI agents using tools and data through MCP.
- Netskope Skylight AI Command Center keeps the inventory of AI apps, MCP servers, and AI identities.
Prioritize the highest-risk AI cases
The agent prioritizes entities by behavioral risk instead of alert volume. Repeated violations, persistent behavior, and deviations from an entity’s established baseline carry the greatest weight. They indicate an emerging pattern and patterns predict business impact, operational disruption, or policy breakdown better than a single alert does. Analysts are able to focus investigation time on the activities and agents that are genuinely putting the business at risk, spending less time on low-value alerts.
Investigate each case with evidence
Analysts start each case with the evidence already assembled. For every prioritized entity, the AI Risk AISecOps Agent analyzes activity, correlates related events, builds an evidence timeline, then returns a verdict of risky, legitimate, or inconclusive with a confidence level. The job of an analyst moves faster when they see the full context behind each verdict, and an inconclusive result shows where the evidence is limited.
Because the agent does not sit inline, real-time controls such as AI Guardrails and DLP continue to enforce policy as investigations run, so organizations maintain continuous protection. Analysts keep full control of remediation decisions and act through current controls such as trust-score policies and watchlists, with no new policies or workflows to build.
An investigation reaches its full value when governance teams can translate security findings into policy decisions. The agent displays investigation results in the Netskope Skylight AI Command Center, so security and governance teams work from the same evidence and verdicts and expedite the workflow from risk assessment to policy action.
To put this shared workflow in place, enable AI Risk AISecOps Agent from the Netskope AgentSkope Marketplace. Download the agent data sheet or schedule a demo with our team to see it in action.
