Stay on top of AI security with AI Security Exposure Management
A new module that brings AI security findings into ServiceNow, so you can manage them with the same efficiency as the rest of your security workflows.
Introduction
AI is reshaping the modern workplace at breakneck speed — but rapid adoption opens equally rapid security blind spots. You can’t scale AI innovation without a defense strategy to match it. That’s where ServiceNow’s exciting launch of AI Security Exposure Management comes in. This powerful new solution brings AI security findings and vulnerabilities directly into the platform, so they can be managed right alongside the rest of your security work. AI Security Exposure Management lets your organization defend its AI ecosystem and orchestrate AI security with the same enterprise-grade efficiency you already trust and use today.
If you’re a vulnerability analyst, a security lead fielding a flood of “is this AI tool safe?” requests, or a platform owner trying to keep AI adoption from outrunning AI governance — this one’s for you.
The hours AI saves you shouldn’t turn into hours spent cleaning up AI security issues.
What is AI security?
AI security is the practice of protecting the models, applications, agents, and data that power AI-driven workflows against threats that traditional security tools can’t see. A firewall inspects network traffic, but it can’t recognize a jailbreak attempt hidden in a prompt, a model leaking sensitive data in its response, or an autonomous agent being manipulated into misusing its tools. AI security closes these gaps across the full AI lifecycle — discovering AI assets in use (including unsanctioned “shadow AI”), validating models for hidden vulnerabilities, and enforcing real-time guardrails against threats like prompt injection and data exposure. With AI adoption far outpacing AI governance in most organizations, it has quickly become one of the most urgent priorities in enterprise security.
The ServiceNow solution
ServiceNow’s AI Security Exposure Management (AISEM) is a dedicated module within the Security Exposure Management workspace, giving vulnerability analysts a single, unified view of the entire AI attack surface. Everything lands in one place — the AI Exposures dashboard — where an Overview band tracks your open, unassigned, approaching-target, and overdue findings at a glance, and dedicated views break the work down across AI Vulnerabilities, AI Validation Findings, AI Posture Findings, and a full Inventory of every AI model and API discovered in your environment.
The module ships with prebuilt metrics tuned to the vulnerability categories and security signals that are specific to AI and AI-agent functionality, and it maps findings to industry frameworks like MITRE ATLAS — so analysts can see not just what’s wrong, but how an adversary would exploit it. Paired with the module’s focused view, these out-of-the-box metrics accelerate the remediation of AI security exposures.
Crucially, coverage extends beyond standalone models to the whole agentic stack — the autonomous agents, the tools they can call, their system prompts, and the MCP servers they connect to. And it doesn’t stop at behavior: posture checks ask the provenance questions that are easy to overlook in the rush to ship — is the model’s publisher a verified organization, is its license actually valid for your use, and does it try to run code the moment it’s loaded?
ServiceNow is also rolling out the AI Guardrails Helper, a new Now Assist skill included with the module that helps you identify and manage the runtime guardrails associated with your AI tools — automatically detecting which findings are already protected and which still need your attention.
AISEM also integrates with the AI Control Tower, activating key security and remediation metrics on the AICT Security tab:
Why it matters
If you’re a sci-fi fan like me, you might have an imaginative idea of what AI risk could look like. But here are the real risks AI introduces to an organization today, as captured by the OWASP LLM Top 10 and MITRE ATLAS:
- AI model vulnerabilities (supply chain): Static findings about the model artifact, discovered before deployment.
- Runtime findings: Live detections on prompts and responses in production.
- Agentic findings: Findings on autonomous agents and their tool connections.
- AI Posture findings: Inventory and configuration findings that expose shadow AI, unmanaged assets, and misconfigurations.
- AI Validation findings: Results from automated adversarial testing that emulates MITRE ATLAS attacker techniques — such as an LLM jailbreak — against your own applications, documenting each successful exploit path like a pen-test report before a real adversary finds it.
A validation test might disguise a harmful request inside a simple text-encoding trick to slip past a model’s safety filters. The finding records exactly how the model was coaxed into responding. Or a model scan might surface code buried in a model file that would execute the instant the model is loaded, before it ever serves a single prediction. These are the kinds of risks that never show up in network traffic, and exactly the kinds AISEM is built to catch.
Often an organization adopts a new product and then calls on its security team to “make it secure.” With hundreds or thousands of users and multiple finding types, that could be an enormous task — but not with AI Security Exposure Management. Because AISEM is part of the Unified Security Exposure Management (USEM) suite, you can quickly fold the AI security workflow into your current processes, accelerating your time to value.
How to turn it on
Because AI Security Exposure Management lives within the Unified Security Exposure Management (USEM) suite, turning it on is much like setting up any other Vulnerability Response module. You can install from the AI Security Exposure Management application listing on the ServiceNow Store.
Role required:
- admin
Required plugins:
- Unified Security Exposure Management / Vulnerability Response (sn_vul, v30.0.0+)
- AI Security (sn_sec_ai)
- AI Discovery (sn_ai_disc)
- Now Assist for Vulnerability Response (sn_vul_ai)
At least one AI defense integration (imports AI security exposures):
- Cisco AI Defense - model vulnerabilities and validation findings (automated red-teaming alerts)
- HiddenLayer - model vulnerability data
- Palo Alto Prisma AIRS Exposures - model vulnerability data
At least one AI asset / inventory integration:
- Palo Alto AIRS Discovery - AI model and agent data (if available)
- AI Service Graph Connector for HiddenLayer - AI asset inventory data scanned by HiddenLayer
Once you’ve installed the applications and set up the integrations above, you can fold AI security findings into your existing VR workflows or build new automation from scratch. AI is a highly productive tool that can save countless hours — and ServiceNow’s AI Security Exposure Management helps your organization manage AI security exposures efficiently, so the hours AI saves you don’t turn into hours spent cleaning up AI security issues.
Get started
Ready to bring your AI attack surface into ServiceNow? Spin up AI Security Exposure Management in your instance, point it at your AI defense and discovery integrations, and watch the findings start flowing into a workspace your team already knows. Check the AI SEM product documentation and the latest release notes for setup details and what’s new.
Already trying it out? Drop your questions, use cases, and feedback in the comments — we’d love to hear how you’re putting AI Security Exposure Management to work, and what you’d like to see next.
