Enterprise AI governance and AI risk management built on control evidence
Know which AI your enterprise is using, what risk it creates, and whether required controls are working.
DigitalXForce finds the models, copilots and agents your organization runs and maps each one to the frameworks you report against. It then tests each guardrail as a control and keeps the evidence.
AI reaches an enterprise through cloud services, code repositories, SaaS products with embedded AI and models built in house. A list that someone keeps by hand cannot keep up with that. This page is for the CISO, the chief AI officer and the risk, compliance and legal leaders at a mid-size or large organization who must say which AI the enterprise runs and whether its controls still work.
AI governance is the set of policies, roles, controls and evidence an organization uses to decide which AI systems it runs, how they may be used and whether their controls keep working. DigitalXForce runs AI governance on the same platform as its control assurance. The AI TRiSCM module discovers AI systems, including shadow AI, and assesses each one. It maps each system to NIST AI RMF, ISO/IEC 42001 and the EU AI Act. Guardrails become controls with tests, and a failed guardrail raises a risk with an owner and its evidence attached.
AI systems missing from the inventory and guardrails nobody has retested
- Teams use AI tools and features that never went through a review.
- The AI inventory is a spreadsheet, and it misses models, agents and SaaS features.
- Legal, security and the model owners each run their own review of the same system.
- Evidence for an AI system sits in emails, tickets and shared drives across several teams.
- Someone maps each regulation to each AI system by hand.
- A guardrail was checked when the system was approved, and nobody has checked it since.
What governing AI by spreadsheet costs
An AI system that is not in the inventory is not governed at all. Nobody has assessed it, no control applies to it, and nobody owns its risk.
Separate reviews repeat the work. Legal, security and risk can ask one owner the same questions in different formats and still reach different conclusions. When a regulator, an auditor or the board asks for evidence, someone has to pull it together from emails, tickets and shared drives.
A guardrail can also stop working after approval. A data handling rule can lapse or a model can drift, and nobody sees it until the next scheduled review.
What good AI governance looks like
When AI governance works, one inventory holds every AI system, sanctioned or not. Each system has a use-case tier and an owner. Its obligations are mapped once to the frameworks the organization reports against.
Guardrails for approved use, prohibited use, data handling and human oversight are written as controls, and each control has a test. Each system is assessed on a schedule and again when it changes. A failed test becomes a risk with an owner. The evidence is kept, so an auditor or a regulator can read it later.
How DigitalXForce governs AI on Continuous Control Assurance (CCA)
Continuous Control Assurance (CCA) uses evidence, monitoring and validation to determine whether controls continue to operate as expected. Continuous Control Monitoring (CCM) monitors conditions, evidence and signals associated with controls, and CCM is a capability within CCA. AI governance on DigitalXForce runs on that model. The AI guardrails are controls, and they get the same treatment as any other control.
AI TRiSCM and AI Risk Governance is the DigitalXForce module for AI asset discovery, assessment and mapping to NIST AI RMF, ISO/IEC 42001, the EU AI Act, the OWASP LLM Top 10 and MITRE ATLAS. The table follows one AI system through its lifecycle.
| Step | Stage | What DigitalXForce does |
|---|---|---|
| 1 | Discover and inventory | The module discovers models, copilots, agents and RAG stores across cloud, code repositories, CI/CD pipelines, containers, model endpoints, internal LLMs, third-party SaaS with embedded AI, prompt libraries and data pipelines. Shadow AI appears in the inventory the same way as sanctioned AI. |
| 2 | Classify the use case | Each AI system gets a use-case tier. An AI system is approved before it goes into production. |
| 3 | Map obligations and controls | The module maps each system to NIST AI RMF, ISO/IEC 42001, the EU AI Act, the OWASP LLM Top 10 and MITRE ATLAS. Approved use, prohibited use, data handling and human oversight are expressed as controls with tests. |
| 4 | Assess | Each AI system is assessed for adversarial risk, such as prompt injection and model extraction. The assessment also covers data leakage, model integrity (bias, drift and hallucination) and code and supply chain risk. |
| 5 | Monitor | Assessments run on a schedule and again when the system changes. Each system carries a risk score built from its use-case tier, data sensitivity and autonomy, adjusted by the coverage and currency of its controls. The score breaks down into its parts, so you can see why it moved. |
| 6 | Findings and remediation | A failed guardrail raises a risk in X-ROC, assigns an owner and attaches the evidence. The control is tested again when the fix is marked done, and the finding closes only when the retest passes. AI risks sit in the enterprise risk register. |
| 7 | Evidence and reporting | Every test result is retained with timestamp and source. One evidence set serves the EU AI Act, ISO/IEC 42001 and NIST AI RMF. XForce GPT writes the executive narrative. |
X-ROC, the XForce Risk Operations Center, is the operations layer of the DigitalXForce TRiSCM platform, where control failures, risk changes and vendor events are alerted, triaged, escalated and reported. A failed AI guardrail therefore lands in the same queue as a failed security control.
An analyst reviews AI conclusions before anyone relies on them, with a link back to the evidence used. Certification stays with the accredited auditor or certification body. DigitalXForce hands that auditor the mapped controls and the evidence.
AI governance inside Enterprise Trust, Risk, Security and Compliance Management (TRiSCM)
DigitalXForce is the AI-native Enterprise Trust, Risk, Security and Compliance Management (TRiSCM™) platform unifying automated GRC and security posture management. TRiSCM, or Trust, Risk, Security and Compliance Management, is a category of enterprise software defined by DigitalXForce that converges automated governance, risk and compliance with security posture management into a single real-time system, replacing periodic control validation with continuous control assurance.
The AI TRiSCM module runs on the same data layer as the other 14 modules, and two of them do most of the work for AI. Vendors that supply models are assessed in AI-Powered Third-Party Risk Management. The posture of the systems the AI runs on comes from X-SPM, which is Extended Security Posture Management, the DigitalXForce capability that scores security posture across the enterprise and its vendors from the same control data.
What each capability changes for the team and for executives
| Buyer problem | DigitalXForce capability | Operational result | Executive result |
|---|---|---|---|
| Nobody knows every AI system in use. | AI TRiSCM discovers models, copilots, agents and RAG stores, and shadow AI appears beside sanctioned AI. | The inventory includes systems that never went through a review. | Leaders can answer which AI the enterprise runs. |
| Regulations are mapped to each system by hand. | The module maps each system once to the five AI frameworks it covers. | One evidence set serves the EU AI Act, ISO/IEC 42001 and NIST AI RMF. | Compliance questions get answered from one record. |
| Guardrails are checked once at approval. | Guardrails are expressed as controls with tests, and assessments run on a schedule and when the system changes. | A failed guardrail raises a risk with an owner and its evidence. | Leaders learn about a failed guardrail while it is still open. |
| Legal, security and risk each review the same system. | The module runs on the same data layer as the other 14 modules, and AI risks sit in the enterprise risk register. | Each team reads the same inventory, evidence and risk register. | AI risk appears beside every other enterprise risk. |
| Nobody can explain why an AI risk changed. | Each AI system has a decomposable risk score built from use-case tier, data sensitivity and autonomy. | The reason the score moved is named. | Executives see what changed and why. |
The proof behind the platform
The AI TRiSCM product page lists the five AI frameworks the module maps to. Across the platform, each control is mapped once to every requirement it meets in the 50+ compliance frameworks. DigitalXForce reads evidence through 250+ technology integrations, in two adapter families: X-Connect for security tools and E-Connect for enterprise systems.
Every AI test result is kept with its timestamp and source. Each input can be traced to the tool it came from, the control it belongs to and the date it was read.
Why spreadsheets, policies and separate reviews leave a gap
A spreadsheet inventory is a reasonable start. It lists only the AI that someone reported, and it is out of date soon after someone fills it in.
A written AI policy sets the rules for approved use, prohibited use and data handling. On its own, a policy does not test whether a system follows those rules after approval.
Separate legal, security and model reviews each bring expertise the organization needs. When they run separately, the organization ends up with three records of the same system. Ask whether your current process lets all three teams read the same record.
Periodic AI assessments still have a place, and the auditor or certification body still certifies. A guardrail can stop working between assessments. DigitalXForce tests guardrails on a schedule and when the system changes, so a failure shows up before the next review.
What happens after you ask for a readiness assessment
The demo is a 30 minute walkthrough on your own frameworks and systems. DigitalXForce builds it around the ones you name in the form and replies to every request within 1 business day.
The standard proof of value runs for 4 weeks, with success criteria set with you. Connectors are configured in week 1. The attack surface and External Risk View are set up in week 2. The first assessments run in week 3, and DigitalXForce reviews them with you in week 4. You keep your existing systems unless you decide otherwise.
Management owns the program and one named executive sponsors it. A program owner in risk or compliance runs it, control owners fix what fails, and internal audit reviews independently. No benchmark for headcount exists, so DigitalXForce does not quote one.
The full DigitalXForce platform runs in the client’s own hosting, so the client keeps full control of its data, which is the model DigitalXForce prefers. DigitalXForce Lite is the full DigitalXForce platform hosted in the cloud, with the same functionality, for any organization that prefers cloud hosting. Pricing depends on modules, environment size and deployment scope. Most teams start with a scoped rollout and expand from there.
Questions buyers ask about enterprise AI governance
Does this cover generative AI and agentic AI?
AI TRiSCM inventories and assesses models, copilots and agents, along with RAG stores, internal LLMs, prompt libraries and SaaS products with embedded AI. The assessment covers risks that generative AI raises, such as prompt injection, data leakage and hallucination, and it applies to agents as it does to models and copilots.
How is AI inventoried?
The module discovers models, copilots, agents and RAG stores across cloud, code repositories, CI/CD pipelines, containers, model endpoints, internal LLMs, third-party SaaS with embedded AI, prompt libraries and data pipelines. Shadow AI appears in the inventory the same way as sanctioned AI. An AI system is approved before it goes into production.
Which frameworks does it map to?
AI TRiSCM maps each AI system to NIST AI RMF, ISO/IEC 42001, the EU AI Act, the OWASP LLM Top 10 and MITRE ATLAS. One evidence set serves the EU AI Act, ISO/IEC 42001 and NIST AI RMF. Certification stays with the accredited auditor or certification body, and DigitalXForce hands that auditor the mapped controls and the evidence.
How are AI controls monitored?
Approved use, prohibited use, data handling and human oversight are expressed as controls with tests. Each AI system is assessed on a schedule and again when the system changes. A failed guardrail raises a risk in X-ROC, assigns an owner and attaches the evidence, and the finding closes only when the retest passes.
Can legal, security and risk teams share one record?
The AI TRiSCM module runs on the same data layer as the other 14 modules, and AI risks sit in the enterprise risk register. Every test result keeps its timestamp and source. Legal, security and risk teams read the same inventory, the same evidence and the same register.
Find out how ready your AI governance is
A 30 minute walkthrough shows how DigitalXForce would inventory your AI systems, map them to the frameworks you name and test their guardrails as controls.
