Automated documentation
Track model history with less manual documentation
watsonx.governance records configured data sources, prompt templates, code versions, and test results. Your team can generate a consolidated record for review.
AI GOVERNANCE & SAFETY · POWERED BY IBM WATSONX.GOVERNANCE
Deploy AI with boundaries, monitoring and automated regulatory audit trails
Deploying AI across business operations requires more than prompt guidelines. GrandView implements IBM watsonx.governance to track registered models, evaluate accuracy, detect drift and bias, and support audit documentation. You protect confidential company data while keeping business initiatives moving forward safely.
Proven controls for enterprise AI across hybrid cloud environments
Implementing AI safety does not mean freezing innovation. Working with IBM and Red Hat, we help organizations set clear rules, monitor live model performance, and support regulatory reviews while limiting disruption to engineering teams.
Continuous monitoring
Monitor drift, bias, and quality thresholds
Monitor configured metrics after deployment. The system evaluates answer quality, faithfulness, and fairness, and alerts your team when defined thresholds are crossed.
Multi-model oversight
Govern models across multiple clouds from one console
Apply consistent governance policies across IBM Granite, Azure OpenAI, Amazon Bedrock, and privately hosted open source models. You maintain central control while reducing dependence on a single model vendor.
The result is not another layer of administrative overhead. It is confidence that your AI initiatives comply with internal policies and external regulations from day one.
Discuss your governance priorities →What makes trusting AI with your company data harder right now?
Most enterprise AI risks do not come from malicious intent. They come from informal deployments, unmonitored model updates, and a lack of clear tracking across departments.
Teams use separate AI tools with no central record
Individual business units test public and private AI tools independently. Leadership is left without an accurate inventory of what models are active, what data they access, or where company files are shared.
Establish a centralized enterprise inventory of all active models, prompt templates, and data endpoints, restoring total visibility to IT and compliance leadership.
Generative models produce answers that sound confident but are wrong
Without automated faithfulness testing, language models can invent facts or misinterpret technical policies. Relying on unverified answers in underwriting, contracts, or customer service creates immediate liability.
Implement automated evaluation pipelines that test answer relevance, source faithfulness, and hallucination rates against verified company knowledge bases.
Different AI risk regimes require different evidence and controls
The EU AI Act creates legal obligations that vary by use case, risk classification, and organizational role. NIST AI RMF provides a voluntary operating model through Govern, Map, Measure, and Manage. Relevant U.S. banking organizations may also consider current interagency model risk management guidance. Each regime requires a distinct control and evidence mapping.
Map each AI system to its specific regulatory duties and voluntary framework outcomes, creating defensible documentation without one-size-fits-all overengineering.
Models lose accuracy as business conditions change
A model that scored well during initial testing can degrade as customer habits and market data shift. Without ongoing monitoring, accuracy drops may go unnoticed and affect business transactions.
Configure continuous production telemetry to monitor data drift, concept drift, and output fairness, alerting engineering teams the moment metrics deviate.
A controlled, step-by-step transition
Catalogue active models and prompts Evaluate accuracy, drift, and bias Automate audit records and compliance
Because ungoverned AI, unmonitored updates, and fragmented tools reinforce each other, GrandView establishes a central governance control plane before operational risks compound.
How automated governance compares to traditional manual reviews From periodic questionnaires to continuous model telemetry
| Governance Capability | Traditional / Manual Review | GrandView watsonx.governance Implementation |
|---|---|---|
| 01 Model Inventory | Fragmented spreadsheets maintained manually by individual teams, leaving leadership unaware of active models and third-party API dependencies. | Unified enterprise catalog for registered models, versions, prompt templates, data sources, and designated business owners. |
| 02 Accuracy & Faithfulness | Occasional spot checks after user complaints or audit requests, allowing silent hallucinations to reach clients and create immediate legal liability. | Continuous automated testing measuring whether answers match reference files and factually grounded source records. |
| 03 Drift Detection | Discovered months later during periodic performance reviews after degraded model scoring has already compromised business decisions. | Configured monitoring alerts engineers the moment concept decay or data distribution shifts cross defined thresholds. |
| 04 Fairness & Bias Tracking | Static pre-launch reviews that completely miss ongoing demographic shifts and real-world runtime discrepancies. | Ongoing runtime evaluations checking model outcomes against protected attributes with automated threshold alerts. |
| 05 Documentation & Audits | Weeks spent compiling emails, pull requests, and test logs under frantic panic before legal, supervisory, or internal compliance audits. | Automated AI Factsheets updated continuously across the model lifecycle, recording versions, evaluations, and approvals. |
| 06 Multi-Cloud Governance | Incompatible tools and fragmented metrics across separate clouds, locking organizations into vendor silos or fractured monitoring. | Single control plane for IBM Granite, Azure OpenAI, AWS Bedrock, and privately hosted open source models on OpenShift. |
| 07 Regulatory Readiness | Unclear obligations and incomplete evidence creating exposure to fines, supervisory scrutiny, or stalled business rollout. | Clear mapping of applicable EU AI Act duties, voluntary NIST AI RMF alignment, and direct integration with IBM OpenPages GRC. |
EXECUTIVE PERSPECTIVE & ACCOUNTABILITY
CLEAR ANSWERS FOR LEADERS RESPONSIBLE FOR RISK, SECURITY, AND TECHNOLOGY
AI governance touches multiple disciplines. We align your risk committee, security officers, legal team, and IT leaders around shared metrics and clear accountability.
Chief Risk Officer & Head of Model Risk
Maintain a centralized model inventory with enforceable risk thresholds
Untracked spreadsheets, unknown model deployments across business units, and manual panic when supervisory inquiries or model risk reviews arrive.
See which models are registered, assign risk tiers based on business impact, and receive automated alerts when configured accuracy or fairness thresholds are crossed.
CISO & Data Privacy Officer
Reduce the risk of confidential data exposure and unauthorized model access
Staff pasting proprietary company code and customer records into unvetted public LLMs without access controls, retention boundaries, or audit logs.
Verify that sensitive files and customer records use role-based access. Configure third-party models with approved access, logging, and data retention policies.
General Counsel & Compliance Leaders
Support EU AI Act readiness and NIST AI RMF alignment with verifiable records
Ambiguity over whether models qualify as high-risk under EU AI Act, scattered compliance documentation, and weeks spent compiling evidence for legal review.
Determine which obligations apply to each AI use case based on role and risk classification. Maintain evidence for legal duties while using NIST AI RMF to structure governance.
CIO & Enterprise Architects
Run a unified governance framework across hybrid and multi-cloud environments
Deploying separate, siloed monitoring tools for IBM, Azure OpenAI, AWS Bedrock, and open-source models, fracturing enterprise architecture.
Manage IBM watsonx, Azure OpenAI, AWS Bedrock, and private models on OpenShift through one central control plane without slowing down engineering sprints.
The assessment establishes an agreed governance baseline across departments. Implementation follows a phased sequence without disrupting ongoing development.
Explore the governance assessment ↓AI Governance & Model Lifecycle Architecture
How the watsonx.governance control plane operates across your AI lifecycle
From early model selection to production monitoring, the platform supports policy controls across selected lifecycle stages.
Centralized Model Registration
watsonx.governance · Catalog
Business Intended Use
Claims Underwriting & Policy Search
System Prompts Registered
24 Versioned System Templates
Grounding Data Corpus
Verified Policy Documents (CP4D)
Lineage Tracking
Git Commit & Data Hash Bound ✓
Registered in enterprise inventory with approved risk tier
100% Tracked
Continuous Runtime Telemetry
watsonx.governance · Monitor
Faithfulness & Factuality
98.6% (Target > 95.0%)
Answer Relevance
0.94 / 1.00 Factual Overlap
Concept & Data Drift
1.2% Shift (Alert at > 5.0%)
Fairness & Bias Variance
Zero Protected Attribute Skew ✓
Continuous evaluation active; real-time alert triggers set
Telemetry Healthy ✓
Automated Factsheet & Audit Lineage
OpenPages GRC Integration
Regulatory Alignment
EU AI Act Art. 8-15 & NIST AI RMF
Supervisory Evidence
Evaluation Logs & Approval Sign-Offs
Model Governance State
Approved for Production Release
Tamper-Proof Audit Trail
Cryptographic Lineage Verified ✓
Auto-generated Factsheets ready for risk & compliance review
Audit Certified ✓
CLICK STAGES ABOVE OR RIGHT TO EXPLORE GOVERNANCE CAPABILITIES
Catalog governed models, prompts, and use cases
AI projects within the agreed scope are registered with their intended business purpose, owner, and risk classification. Connectors link each use case to code repositories, data endpoints, and prompt libraries across any cloud.
WHAT YOU GAIN
Unified enterprise inventory across IBM, Azure, and AWS
Full visibility into who built each model, what company data it accesses, and its designated risk classification.
Test performance in pre-production and live runtime
Automated pipelines evaluate models before release and track them continuously in production. Metrics include data drift, concept drift, answer relevance, faithfulness, and toxic content.
WHAT BECOMES AUTOMATIC
Continuous drift alerts, hallucination scoring, and fairness monitoring
Automated evaluations alert your teams before flawed answers impact business operations or customer decisions.
Generate versioned AI Factsheets for auditors and leaders
The system logs evaluation metrics, version changes, and sign-offs into automated Factsheets. Reports export directly into risk management systems like IBM OpenPages or executive dashboards.
WHAT YOU REGAIN
Audit-ready provenance, supervisory compliance, and defensible records
Defensible evidence ready for internal audits, supervisory reviews, and EU AI Act readiness.
A clear basis for your enterprise AI governance strategy
You receive a documented inventory of active models, automated continuous evaluation pipelines, and a verified governance control matrix—connecting compliance obligations with daily operations.
ENTERPRISE AI GOVERNANCE ADVISORY
Enterprise AI model catalog & registry
IBM Granite & Open Source
Private Cloud Deployments
Azure OpenAI & Bedrock
External Hosted Endpoints
watsonx.governance
CENTRAL CONTROL PLANE
OpenPages GRC
Model Risk Inventory
Operational Workflows
Governed Execution ✓
UNIFIED ENTERPRISE MODEL CATALOG
01 / 03
A searchable inventory of registered models, prompt templates, and data sources within the agreed scope. We catalog business purpose, owner, lifecycle state, and designated risk tier in one place.
ENTERPRISE AI GOVERNANCE ADVISORY
Automated AI Factsheets & audit pipeline
| REGISTERED MODEL | MONITORED METRIC | RUNTIME SCORE | GOVERNANCE STATE |
|---|---|---|---|
| Claims Policy Assistant watsonx.ai runtime • v2.4 | Faithfulness & RAG Guard Hallucination drift monitor | 98.6% (Pass > 95%) | Factsheet Logged ✓ |
| Risk Scoring Engine Azure ML endpoint • v3.1 | Concept & Data Drift Production baseline: Q4 | 1.2% (Alert > 5%) | Active Baseline ✓ |
| Customer Support LLM AWS Bedrock • Claude 3.5 | Protected Attribute Disparity Demographic parity verify | 0.00% Variance | CRO Signed ✓ |
CONTINUOUS DIGITAL AUDIT LINEAGE
02 / 03
Continuously updated digital records detailing model lineage, evaluation scores, and deployment approvals. AI Factsheets capture the history required for internal compliance reviews and external audits.
ENTERPRISE AI GOVERNANCE ADVISORY
Regulatory & governance control matrix
01
EU AI Act Scope
Classification of use cases, role duties, and Annex III high-risk requirements
02
NIST AI RMF Alignment
Voluntary operational practices across Govern, Map, Measure, and Manage
03
Supervisory Model Risk
Sector-specific guidance and direct bridge into IBM OpenPages GRC
COMPLIANCE AND VOLUNTARY FRAMEWORK MAPPING
03 / 03
A practical mapping that separates applicable EU AI Act duties, voluntary NIST AI RMF outcomes, and sector-specific banking guidance where relevant—establishing clarity across your risk, legal, and engineering teams.
From assessment to governed AIWork with GrandView to turn regulatory uncertainty into a lean, automated governance framework.
Schedule your governance assessmentOur structured approach to deploying enterprise AI governance
We deploy IBM watsonx.governance using a four-step path that establishes baseline control while limiting disruption to ongoing projects.
Catalogue existing AI initiatives and map requirements (Weeks 1–2)
We catalogue your existing AI initiatives, review prompt libraries and model endpoints, identify untracked tools, and map your regulatory requirements across business units.
Configure architecture, risk tiers, and policies (Weeks 3–4)
We configure IBM watsonx.governance on your hybrid cloud or Red Hat OpenShift environment. We establish risk tiers, drift thresholds, and approval workflows tailored to your policies.
Connect models and generate baseline Factsheets (Weeks 5–6)
We connect your first wave of production models, whether hosted on IBM watsonx, Azure OpenAI, AWS, or on-premises, and run initial tests to generate baseline Factsheets.
Activate continuous telemetry and operational handover (Weeks 7–8)
We configure alert thresholds and schedules, connect reporting into your risk and GRC workflows, and train your risk, legal, and engineering teams to manage ongoing governance independently.
DISCUSS YOUR AI GOVERNANCE REQUIREMENTS
Bring your current AI use cases, upcoming regulatory deadlines, or model monitoring challenges to a direct conversation with our practice leads. No junior sales reps, just practical architecture.
Steve Johnson
VP of Business Automation & Integration
Practice Lead · GrandView
IBM watsonx.governance & Model Risk Governance Expertise