Gartner’s How to Achieve the Minimum Viable AI Governance
Report
AI Risk Exposure Management: Top 10 Focus Areas for 2026
A practical report on AI risk exposure management, examining the enterprise AI applications, identities, permissions, agents, integrations, and models most likely to create exploitable exposure.
Lock Down These Enterprise AI Exposures to Level Up
AI exposure spans applications, data access, non-human identities, MCP infrastructure, plugins, and model supply chains. These technologies inherit permissions, access sensitive data, invoke external services, retain memory, and increasingly act autonomously.
Traditional application security, identity governance, and endpoint controls were not designed to manage this new exposure class.
In this research report, Remedio examines the ten AI exposures most likely to exist across enterprise environments today and most likely to be exploited over the coming year.
For each exposure, we provide practical guidance to help security teams continuously discover, prioritize, govern, and remediate AI risk.
Download the report to learn:
- Which AI exposures create the greatest enterprise blast radius
- How permissions, integrations, and autonomous actions compound AI risk
- Where prompt injection, MCP servers, plugins, and model supply chains defeat conventional controls
- Why periodic governance cannot keep pace with continuously changing AI environments
- How continuous enforcement and safe remediation reduce the time exposure remains exploitable
A New Age of Risk Demands A New Form of Risk Management
Rather than focusing solely on policy and oversight, AI risk exposure management addresses the operational reality: what AI is running, what it can access, what actions it can take, where controls have drifted, and how unsafe conditions can be remediated before they are exploited.
Battle Shadow With Light
Get a clear understanding of where risks exist across AI tools, identity and access systems, enterprise data environments, agent frameworks, MCP infrastructure, and AI supply chains.
Stem the Bleeding
98% of organizations have unsanctioned AI use. At the same time, AI-related breaches are becoming more common while bearing greater financial costs. Take back control.
Govern Without Compromise
With access to corporate data, business systems, code repositories, cloud environments, and communication platforms, any AI compromise risks organization-wide fallout. Context-aware governance keeps you safe and productive.
Testimonials Section
Remedio gives us the ability to fix problems in our environments without impacting our operations; it’s a real game-changer.
Remedio has helped me deploy a Technical Security Baseline to all my endpoint devices globally.
Remedio gives our team incredibly detailed visibility into our global computing environment.
Remedio helps me close security gaps – including those I didn’t know I had.
FAQs to Mull Over Before You Get Started
What is AI risk exposure management?
AI risk exposure management is the continuous discovery, assessment, prioritization, governance, and remediation of security exposure introduced by AI tools, agents, identities, integrations, models, and data access.
How is AI risk exposure management different from AI governance?
AI governance defines policies, accountability, acceptable use, and oversight. AI risk exposure management operationalizes those requirements by identifying actual AI usage, evaluating permissions and configurations, constraining unsafe behavior, correcting drift, and remediating exploitable conditions.
What types of exposure does enterprise AI create?
Enterprise AI exposure includes shadow AI, excessive agent permissions, prompt injection, exposed MCP servers, overshared Copilot-accessible data, insecure plugins, sensitive output disclosure, poisoned models, persistent memory, and excessive autonomous agency.
Why do traditional security tools struggle with AI exposure?
Traditional tools generally observe isolated layers. Endpoint tools may see processes but not agent intent. Identity tools may see permissions but not AI tool behavior. CASBs may miss personal accounts and local models. SIEMs cannot analyze activity that upstream controls do not capture. AI exposure therefore persists across control boundaries.
How should organizations manage AI risk exposure?
Organizations should continuously inventory AI usage, govern agent and application identities, apply least privilege, validate integrations, restrict high-impact actions, monitor configuration drift, enforce approved states, and safely remediate deviations.
