# AI governance operationalised with Deeploy > ServiceNow orchestrates governance workflows. Deeploy connects to live models with real-time monitoring, explainability, and regulator-ready evidence. **Source:** https://deeploy.ai/compare-servicenow/ **About this file:** Machine-readable markdown version of the page above, for LLMs and AI assistants. Regenerated automatically whenever the page is updated. Go beyond workflow oversight in ServiceNow. Deeploy plugs into your AI stack, connects to live models, enforces governance controls, monitors performance with explainability and bias checks, and generates regulator-ready evidence. ## ServiceNow oversees your IT stack. Deeploy is the control layer for your AI. - Catalogs vs connections: ServiceNow lists AI systems, Deeploy connects directly to models in production. - Visibility at the model level: Without model-level monitoring, drift, bias, and unexpected behaviour go unnoticed. - One view of truth: Compliance sees policy. Engineering sees models. Deeploy unifies operational risk, evidence, and performance. ## How Deeploy complements ServiceNow ServiceNow orchestrates. Deeploy verifies and proves. - Onboard any model in your organisation. - Track performance metrics in real-time. - Log and explain model decisions. - Create your own governance controls and apply them across models. - Use pre-made templates to ensure compliance with the EU AI Act & ISO 42001. - Automatically collect audit trails across models and teams. - Compile compliance documentation, model cards and data cards. - Push model status, risk, and evidence back into ServiceNow records. ## Deeploy and ServiceNow at a glance A side-by-side comparison of governance capabilities, technical depth, and regulatory readiness | Feature | Deeploy | ServiceNow | | --- | --- | --- | | AI Use Case Inventory | Yes | Yes | | Workflow & Case Management | n/a | Yes | | Model Explainability | Native, regulator-ready | n/a | | Bias & Drift Monitoring | Built-in | n/a | | Audit Logs (per prediction) | Yes | n/a | | EU AI Act Alignment | Model-level & operational | Policy-level only | | Stakeholder Dashboards | For business, compliance & tech teams | Compliance focus | | AI Model Deployment | Yes | n/a | ## All your AI models in one place **Connect any AI, on any platform, and track them all from one interface.** - Integrates with any MLOps and GenAI platform - Supports any open source framework - Centralised control without migration pains ## Translate policies to controls **Translate your AI policies into controls that directly apply to models.** - Create your own controls & checks - Apply them directly to AI models - Check whether controls & checks are being fulfilled ## Catch issues before they become liabilities **Track performance, explain every prediction, detect drift, and stay in control as your data changes.** - Live dashboards and alerts - Real-time explanations for any AI - Spot issues before it impacts users ## Prove compliance with regulations **Stay ahead of audits without slowing teams down** - Meet EU AI Act & ISO 42001 standards - Use our templates, comply 90% faster - Audit trails & documentation in one place ## Why teams add Deeploy to ServiceNow ### Model-level depth **Deeploy delivers explainability, bias detection, and drift monitoring that ServiceNow's workflow-focused platform cannot provide for technical AI governance.** ### Compliance by design **Built specifically for EU AI Act and ISO standards, Deeploy generates the technical evidence auditors require, not just process documentation.** ### Bridging business & technical teams **Deeploy bridges ML engineering teams with compliance officers through unified dashboards that show both technical performance and regulatory alignment.** ## Ready to see model-level AI governance in action? Book a demo: https://deeploy.ai/book-demo/ ## Frequently asked questions ### We already use ServiceNow for AI governance. Why do we need Deeploy? ServiceNow manages your compliance workflows, tracking processes, documentation, approvals, and risk cases. Deeploy generates the technical compliance evidence that flows into those workflows. When ServiceNow triggers an AI model review, Deeploy provides the bias metrics, drift reports, explainability documentation, and audit trails that fulfil the review requirements. ServiceNow orchestrates governance; Deeploy ensures your AI models are explainable, compliant, and regulator-ready. ### We want to keep governance centralised in ServiceNow. Doesn't adding Deeploy fragment our governance view? Not at all. Deeploy operates as the technical layer that generates AI-specific compliance data and pushes these compliance results directly into your existing workflows. In this way, you ensure a single pane of glass for governance. ### Isn't there overlap between what ServiceNow does and what Deeploy does? Minimal overlap, they're complementary. ServiceNow = workflow and process management (approvals, tickets, risk case tracking). Deeploy = model governance and evidence generation (bias monitoring, drift detection, explainability, prediction logs). ServiceNow tracks *that* you need to govern a high-risk AI model. Deeploy provides the *technical evidence* that the model is compliant, which then flows back into ServiceNow's risk management workflow. ### What specific compliance needs does Deeploy address? Deeploy allows teams to easily set up governance controls at the model level, store model and compliance documentation, monitor performance, and generate audit trails aligned with what regulations such as the EU AI Act require. ### How is this different from just building AI governance capabilities into ServiceNow? You need operational infrastructure where models actually run to generate real compliance evidence. ServiceNow manages workflows; Deeploy deploys and governs models. You can't get prediction-level audit logs, real-time bias monitoring, or drift detection without models running on governance-enabled infrastructure. Deeploy provides that operational capability, then feeds the results into ServiceNow's process management. --- **Get in touch:** You can book a demo at https://deeploy.ai/book-demo/ or reach the team via https://deeploy.ai/contact/.