AI governance for enterprise teams
AI governance platform software for enterprise teams managing the full lifecycle.
SentinelAI connects AI systems, models, datasets, prompts, releases, telemetry, controls, and evidence in one AI governance platform so teams can assess impact, prove oversight, and stay aligned as AI changes.
Operating signals
Understand the differentiator before you drill into the rest of the story.
These proof points explain how SentinelAI turns fragmented AI governance work into a connected operating system with earlier visibility, clearer evidence, and graph-backed impact understanding.
Connected governance layer
Graph-backed relationships
Connect models, use cases, datasets, controls, and governance evidence so teams can understand downstream impact without rebuilding the story manually.
System of record
Intake to evidence in one workflow
Keep intake, approvals, obligations, remediation, and evidence attached to the AI systems they describe.
Framework alignment
Across every major governance framework
Support programs mapped to the EU AI Act, NIST AI RMF, and ISO 42001 without splitting obligations into separate tools.
Buyer and executive visibility
Proof for oversight, diligence, and reporting
Prepare stakeholder updates, buyer trust reviews, and governance conversations from the same connected records.
Category fit
What teams expect from AI governance platform software
High-intent buyers usually want one place to govern AI inventory, reviews, approvals, evidence, and live operating change. SentinelAI is designed to bring those workflows together instead of splitting them across multiple trackers.
If you are comparing AI governance software, the strongest evaluation points are usually governed inventory, dataset and lineage context, approval workflows, release controls, monitoring signals, and stakeholder-ready reporting. SentinelAI brings those capabilities into one operating layer with shared records and internal links.
Choose your path
Follow the story that matches how you evaluate governance platforms.
Most teams want the same outcome, but not the same first step. Start with the platform, obligations, persona workflow, or trust materials based on what you need to prove next.
Platform
See the operating model
Start with the end-to-end platform story if you want the clearest picture of how SentinelAI connects intake, controls, monitoring, and evidence.
Explore platformFrameworks
Map to governance obligations
Review the framework lens if your team is evaluating SentinelAI against the EU AI Act, NIST AI RMF, or ISO 42001 operating needs.
Review frameworksPersonas
Follow your team's workflow
Choose the persona path if you want to understand how compliance, security, risk, and ML teams use the same governed records differently.
See persona viewsResources
Go deeper on trust and proof
Open resources when you need documentation, release notes, and trust material to support deeper diligence or internal sharing.
Open resourcesCategory guide
Understand what an AI governance platform should include
Use the category page if you want a direct explanation of what AI governance platform software should cover and how SentinelAI approaches the problem.
Read the platform guideValue and problem framing
Replace fragmented AI oversight with a clearer operating model.
Start with the core governance problem and expand into the workflow details that matter to your team.
Why teams struggle todayWhen AI governance work lives across spreadsheets, inboxes, ticket queues, and point tools, teams spend more time chasing updates than managing risk.
When AI governance work lives across spreadsheets, inboxes, ticket queues, and point tools, teams spend more time chasing updates than managing risk.
- Reviews slow down because context lives in multiple systems.
- Evidence gets lost between intake, approvals, and follow-up work.
- Leadership visibility depends on manual rollups.
What SentinelAI centralizesSentinelAI brings inventory, intake, approvals, semantic control, monitoring context, and reporting into one operating layer.
SentinelAI brings inventory, intake, approvals, semantic control, monitoring context, and reporting into one operating layer.
Expected outcomesA governed operating model helps teams reduce manual chasing, improve evidence quality, and keep oversight connected to real AI delivery.
A governed operating model helps teams reduce manual chasing, improve evidence quality, and keep oversight connected to real AI delivery.
- Reduce manual status chasing across compliance, security, ML, and business stakeholders.
- Shorten the time needed to register new AI initiatives and prepare them for governance review.
- Help teams move from data-governance ambiguity into explainable taxonomy, ontology, and graph-backed operations.
- Give CISOs and risk leaders a clearer view of AI posture without waiting for custom spreadsheets.
Capability overview
Coverage across the product capabilities governance teams ask for first.
SentinelAI brings together the operational areas that usually live in separate systems, while keeping the language focused on governance support rather than guarantees.
Model and use-case intakeRegister AI models and use cases with ownership, business purpose, lifecycle context, and supporting governance detail from the start.
Register AI models and use cases with ownership, business purpose, lifecycle context, and supporting governance detail from the start.
Taxonomy-backed data governanceTrack datasets, classifications, lineage, approvals, and stewardship with a shared taxonomy instead of fragmented labels and spreadsheets.
Track datasets, classifications, lineage, approvals, and stewardship with a shared taxonomy instead of fragmented labels and spreadsheets.
Semantic relationship operationsConnect use cases, models, datasets, controls, and governance documents through ontology-managed relationships and graph-backed impact views.
Connect use cases, models, datasets, controls, and governance documents through ontology-managed relationships and graph-backed impact views.
Reporting and diligence supportPrepare executive updates, audit conversations, procurement responses, and buyer trust reviews with clearer evidence trails.
Prepare executive updates, audit conversations, procurement responses, and buyer trust reviews with clearer evidence trails.
Governance lifecycle
A practical lifecycle for oversight before and after deployment.
Use a repeatable process that carries governance from initial intake through semantic organization, monitoring, and evidence preparation instead of treating review as a one-time checkpoint.
Register and classifyStep 01Create a shared system of record for AI use cases, models, datasets, and vendors with ownership, intended use, business context, and risk posture.
Create a shared system of record for AI use cases, models, datasets, and vendors with ownership, intended use, business context, and risk posture.
Standardize terms and relationshipsStep 02Use taxonomy, ontology, and controlled relationships so governance teams can classify assets consistently and understand how records influence one another.
Use taxonomy, ontology, and controlled relationships so governance teams can classify assets consistently and understand how records influence one another.
Review, approve, and adaptStep 03Collect evidence, coordinate policy checks, monitor change, and capture role-based approvals so oversight continues before and after launch.
Collect evidence, coordinate policy checks, monitor change, and capture role-based approvals so oversight continues before and after launch.
Report and evidenceStep 04Maintain traceable records, decisions, and summaries that support executive review, procurement diligence, and audit preparation.
Maintain traceable records, decisions, and summaries that support executive review, procurement diligence, and audit preparation.
Trust and frameworks preview
Support framework-aligned governance and enterprise diligence with better records.
Use the trust path that matches your evaluation stage and the frameworks your program already references.
Framework-aligned governance supportSentinelAI helps teams organize governance work in a way that can support programs aligned to key AI governance frameworks.
SentinelAI helps teams organize governance work in a way that can support programs aligned to key AI governance frameworks.
- EU AI Act
- NIST AI RMF
- ISO 42001
Conversion pathsChoose the next step that matches your evaluation stage.
Choose the next step that matches your evaluation stage.
Who it is for
Built for the teams carrying governance, risk, and assurance work together.
Different stakeholders need different entry points, but they benefit most when they share the same governed records.
Compliance officers and AI governance teamsStandardize review workflows, preserve evidence, and map governance work to internal policy expectations, external frameworks, and shared semantic controls.
Standardize review workflows, preserve evidence, and map governance work to internal policy expectations, external frameworks, and shared semantic controls.
CISOs and risk leadersSee AI inventory, control coverage, unresolved issues, and vendor exposure in a way that supports portfolio-level oversight.
See AI inventory, control coverage, unresolved issues, and vendor exposure in a way that supports portfolio-level oversight.
Data science and ML teamsDocument models, use cases, datasets, lineage, approvals, and monitoring context while reducing last-minute requests for governance information.
Document models, use cases, datasets, lineage, approvals, and monitoring context while reducing last-minute requests for governance information.
Executives and procurement stakeholdersAccess clearer summaries for customer trust conversations, buying diligence, and board or steering committee updates.
Access clearer summaries for customer trust conversations, buying diligence, and board or steering committee updates.
Next step
Choose the SentinelAI path that fits your evaluation stage.
Book a live walkthrough, start a trial in the application, or review the documentation to see how the platform supports an enterprise AI governance operating model.