Governance today delays data science and AI initiatives, wastes resources, and leaves too much residual risk. Models take twice as long to validate than build, as validation happens after the fact, requires manual work in bespoke tools, and enterprises take more than a year to comply with a single regulation because of the bespoke approach to governance.
Over 45% anticipate regulatory violations, reputational risks, and delayed innovation due to inadequate AI governance (Domino's REVelate Survey), and 95% of enterprises must replace, re-write, or update their AI governance frameworks and processes for today's AI landscape (BARC Study). Why?
Policies and regulations are often abstract legal documents based on high-level principles, mismatched to the reality of data science or AI work. This makes it impossible to enforce policies.
Today, work like model validation uses bespoke and old-fashioned tools such as spreadsheets for each technology or use case. Audits take months to reproduce model lifecycles as new projects while enterprises remain exposed. Data science efforts are sacrificed on model validation and manual audits instead of fueling innovation.
Governance work is systematically bolted onto data science processes, not integrated — model validation lasts months beyond development, impeding innovation and fueling data scientists’ frustration.
Automate and orchestrate the collection, review, and tracing of materials required to ensure compliance with internal and external policies to mitigate risk and drive more value.
Save time and guarantee compliance by using customizable policy templates or creating custom policies and activating them where AI builders work.
Simplify enforcement of any internal or external policy with automated scripts that validate policy adherence.
Receive the context behind AI work and all packaged documentation needed to accelerate reviews and audits.
Governance dashboard
Global visibility with one place to see policies, status of compliance, and open actions across all projects and models. Domino Governance automation reduces the governed model lifecycle by 70%, and shortens the time to comply with new policies from months to weeks.
Model lifecycle management
Manage all policies, collect, and review all evidence and the context of work, enforce policies with automated scripts, and activate approval processes across the AI lifecycle.
Policy templates
Accelerate compliance with customizable off-the-shelf policy reference templates like MRM, EU AI Act, NIST, or define and build your policies from scratch and embed and enforce policies where builders work for a seamless experience.
Project Templates
Enforce best practices by incorporating your policies into every project and model with standard checklists, evaluation tools, automated evidence collection, and codified conventions that increase speed and consistency.
Guaranteed reproducibility
Domino tracks all model lineage by automatically versioning code, data, environments, and results for guaranteed reproducibility and compliance. Domino can support GXP-compliant and non-GXP on a single platform.
Model Cards
Domino improves transparency by automatically recording models' purposes, lineage, downstream usage, performance, limitations, and biases. This allows teams to evaluate whether models are ready for production, and mitigate risks.
Model Registry
View all of your models with their status, metadata, and evolution (e.g., code, data, parameters, environments, results, versions) for auditability and tracking. Never lose track of your models, whether developed in Domino or not.
Model Staging
Verify that models are tested and evaluated in isolated non-production environments and view all models and their status in a single pane of glass.
Model Monitoring
Score model accuracy and relevance, detect model drift, automatically capture predictions, and incorporate human validation so models perform as expected for stakeholders.
Access control and audit logging
Domino authenticates and authorizes users with credential management, and automatically tracks data lineage so reviewers can see who accessed which data, and when, to confidently approve models for production.
