AI security at ServiceNow

AI security: smiling woman holding a closed laptop and looking out an office window

Agentic AI is reshaping how work gets done, unlocking unprecedented speed and scale in enterprise operations.

AI agents can access sensitive data, interact with core systems, and make choices based on dynamic inputs—with human oversight. But without robust security and ethical safeguards, this technology could unintentionally put work at risk.

At ServiceNow, we believe organizations must approach AI security not as a technical checklist, but as a strategic foundation for trustworthy innovation. We’re striving to set an example with how we approach AI security.

Designing secure autonomy

The foundation of secure AI lies in how autonomy is defined and managed. Every AI agent we build at ServiceNow must operate within strict parameters in terms of what it can access, what it can do, and how it’s authenticated.

We clearly define agent roles, limit their data access to what’s essential, and authenticate the interactions they initiate. Think of it as role-based access control (RBAC) for our agentic AI workforce. These controls help ensure AI agents interact only with the data and systems they’re designed to handle—nothing more.

Autonomy isn’t just about giving AI agents freedom to operate; it’s about defining the limits of that freedom. To that end, we design our agents so that their autonomous actions are traceable, communications between systems are encrypted, and only credentialed agents are allowed to interact with sensitive environments. Access and control are prerequisites for scale.

Integrating AI into the workplace is a must. It offers significant benefits—but only when security and ethics are prioritized.

Real-time oversight, real-time defense

Even the best-designed AI agents need supervision. They encounter unpredictable and sometimes adversarial environments. One of the growing threats in this space is prompt injection—where bad actors manipulate agent inputs to trigger unintended actions. Risks like this are subtle but dangerous.

To defend against this, human teams need real-time observability. By continually monitoring AI agent behavior and logging interactions, we’re able to detect anomalies as they occur. This includes tracing anomalous input patterns, unauthorized actions, or unexplained system calls. Isolation techniques such as sandboxing can further reduce risk, helping to ensure that even if an agent is compromised, its impact is contained.

AI agents differ from traditional software and processes because they’re non-deterministic—they act, learn, and adapt in real time, which makes their behavior inherently unpredictable. To ensure their actions remain secure requires monitoring agents that:

Securing the stack

At ServiceNow, AI agents are part of an intricate ecosystem made up of models, APIs, databases, compute environments, and infrastructure. To truly secure AI, we need to secure the entire ecosystem.

Confidential compute technologies are a breakthrough enabling this. They isolate and protect data even when it’s being processed. This is essential when AI agents interact with personally identifiable or regulated information.

At the same time, large language model (LLM) routers can distribute tasks across different models based on cost, performance, and trust levels while applying consistent security protocols such as anonymization and encryption.

Protocols such as A2A (agent-to-agent) and MCP (Model Context Protocol) protocols further extend AI agent functionality—but they also increase attack surfaces. As these protocols and agents evolve, we wrap them in least-privilege access rules, sabotage detection, and authentication layers.

Transparency is key: AI agents must be able to explain their decisions in ways humans can understand, especially in high-stakes environments. When outcomes are unclear, trust erodes.

Prioritizing ethical AI deployment

As AI agents gain autonomy, security alone isn’t enough. That’s why ethical deployment is nonnegotiable for us. Transparency is key: AI agents must be able to explain their decisions in ways humans can understand, especially in high-stakes environments. When outcomes are unclear, trust erodes.

Accountability helps ensure human responsibility remains in the loop at ServiceNow. Even if an AI agent acts independently, someone must govern its impact. Bias mitigation is also critical, as AI agents trained on flawed or imbalanced data can perpetuate harmful patterns. Proactive audits and diverse training inputs help us reduce these risks.

We embed ethics into every phase of AI development and deployment as part of our commitment to fairness, clarity, and responsible progress.

Integrating AI into the workplace is a must. It offers significant benefits—but only when security and ethics are prioritized. The best way to help ensure that is to design secure autonomy, monitor with precision, secure the full stack, and prioritize ethical integrity. These are the pillars of a resilient AI strategy.

Find out how ServiceNow can help you securely put AI to work for people.