AI is taking action
Agents can access enterprise data, invoke APIs, trigger workflows, and act on behalf of users. Governance must control those actions at runtime.
ENTERPRISE AI GOVERNANCE & RISK MANAGEMENT SERVICES
Enterprise AI Governance & Risk
Management Services
Govern AI with confidence.
V-Soft helps enterprises establish, operationalize, and continuously manage AI governance across models, applications, agents, data, and workflows. We bring AI, data, security, cloud, compliance, and engineering together. Governance moves off the policy page and into the systems where AI makes decisions and acts.
Trusted Delivery
Enterprise Delivery
Since 1997
ISO/IEC 27001 & SOC 2 Type II
Certified
ServiceNow
Premier Partner
AI Governance Frameworks
NIST AI RMF
Aligned
ISO/IEC 42001
Aligned
EU AI Act
Readiness
Why now
Why AI Governance Is Now an Engineering Problem
AI is moving from generating answers to accessing data, invoking tools, making decisions, and taking action. Governance cannot rely solely on policies, committees, and periodic reviews. It must extend into the technology, data, applications, and workflows where AI operates.
AI is spreading across the enterprise
AI now exists in applications, copilots, models, agents, workflows, data platforms, and third-party services. You cannot govern what you cannot see.
Regulation now has deadlines
EU AI Act transparency obligations took effect in August 2026. High-risk obligations follow in December 2027 and August 2028, and the preparation window is shorter than it looks.
Aug ’26
Transparency &
enforcement
Dec ’27
Certain high-risk AI systems
(Annex III)
Dec ’28
High-risk AI in regulated products
(Annex I)
Your AI estate
Your AI Governance Needs to Evolve with Your AI Estate
You cannot govern what you cannot see. AI governance must extend across every environment where AI is built, accessed, embedded, or deployed.
AI Models
Foundation models, traditional ML models, LLMs, and third-party models.
AI Applications
Custom and enterprise applications built around AI capabilities.
AI Agents
Systems that can access tools, invoke APIs, interact with applications, and take actions.
Data Environments
Enterprise, customer, employee, operational, and sensitive data accessed or processed by AI.
Business Workflows
Processes where AI recommendations, decisions, or actions influence business outcomes.
Third-Party AI
AI embedded in SaaS platforms, vendor products, APIs, and supplier ecosystems.
Core capabilities
AI Risk Management & Governance Services
Effective AI governance takes more than a policy framework. V-Soft applies Responsible AI principles, AI risk management practices, and governance controls across models, agents, data, applications, and workflows to make AI accountable, secure, and operationally governed.
Structure
Visibility & Risk
Control & Assurance
AI Governance Operating Model
Define how AI governance works across the enterprise.
V-Soft connects governance strategy with the business, technology, risk, security, and compliance teams responsible for putting it into practice.
Governance structure and decision rights- AI council and committee design
- Centralized, federated, and hybrid operating models
- Business, technical, risk and compliance ownership
Governance structure and decision rights- Approval and escalation paths
- Human oversight by risk tier
- Governance KPIs and executive reporting
- AI literacy and role-based responsibilities
AI Discovery, Risk & Regulatory Governance
Know what AI exists, what risk it creates, and what requirements apply.
V-Soft connects AI governance with enterprise data, security, compliance, and technology environments to help organizations govern what they can see and understand.
- AI discovery, inventory and registry
- Shadow AI identification
- AI use-case registration
- Risk classification and dynamic risk scoring
- Business criticality and data sensitivity
- Regulatory applicability assessment
- Third-party and embedded AI discovery
- Regulatory mapping and gap assessment
- AI impact assessments
- Audit and conformity readiness
AI Agent Governance
Govern the systems that can access, decide and act.
V-Soft brings together agent, security, identity, vendor risk, data, and enterprise technology expertise to govern these dependencies across the environment.
- Agent identity and ownership
- Least-privilege access
- Credential and secret management
- Tool, API, and MCP permissions
- Autonomy levels
- Human approval thresholds
- Transaction and action limits
- Runtime policy enforcement
- Kill switches and containment
- Activity logging and traceability
- Agent, skill, and tool change control
AI Vendor & Third-Party Risk
Govern AI introduced through third parties from initial assessment through ongoing change.
[Description]
- Vendor and model assessments
- Embedded AI discovery
- Data, privacy and security review
- Model-provider mapping
- Contractual AI requirements
- Subprocessor mapping
- Ongoing vendor monitoring
- Exit and contingency planning
AI Lifecycle & Control Engineering
Embed governance into the lifecycle instead of reviewing AI after deployment.
V-Soft turns governance requirements into practical controls, workflows, guardrails, and approval mechanisms within the systems where AI operates.
- Use-case intake and approval
- Design and development controls
- Pre-production assessment
- Deployment approval
- Identity, access and data controls
- Runtime guardrails
- Human approval gates
- Model and prompt changes
- Agent and tool changes
- Incident and exception management
- Continuous monitoring
- Retirement and decommissioning
AI Assurance & Security
Test whether AI behaves within defined risk and control boundaries.
V-Soft combines AI security testing, application security, data protection, red teaming, and control effectiveness testing to provide ongoing assurance as AI systems evolve.
- Model and application security testing
- Prompt injection testing
- Jailbreak testing
- Sensitive data leakage testing
- Excessive agency testing
- Insecure tool and API invocation testing
- RAG data-access testing
- Output validation
- Red teaming
- Control effectiveness testing
The V-Soft AI Governance Control Loop
From Governance Strategy
to Continuous Control
We put governance into the flow of AI. The control loop is a continuous lifecycle that turns governance requirements into decisions, enforceable controls, ongoing oversight, and audit-ready evidence.
Governance has no end date.
Every new model, agent, or regulation re-enters the cycle.
Discover
Know what AI is running.
Build visibility across models, agents, applications, copilots, data, integrations, third-party AI, and business use cases.
Classify
Focus governance where risk is highest.
Map each use case to business impact, data sensitivity, autonomy, security exposure, regulatory obligations, and third-party dependencies.
Define
Set the rules of engagement.
Establish ownership, approval paths, human oversight, permitted use, and exception handling for each risk tier.
Enforce
Turn decisions into controls.
Apply practical controls across identity, access, data, agents, APIs, applications, workflows, and environments.
Monitor
Catch risk as it changes.
Track AI activity, agent actions, access changes, policy exceptions, and model or application updates that introduce new risk.
Prove
Show how AI is governed.
Connect decisions, approvals, controls, exceptions, and monitoring into one auditable record.
PLATFORMS & ECOSYSTEM
Governance That Works with the Platforms You Already Run
V-Soft is platform-agnostic, enabling AI governance across your existing technology ecosystem without disrupting or replacing your current platforms.
ServiceNow AI
Control Tower
Microsoft Copilot,
Azure AI Foundry & Purview
AWS Bedrock
Google Cloud
Vertex AI
Salesforce
Agentforce
Specialized AI
governance platforms
ServiceNow AI Governance
Running ServiceNow? V-Soft helps you evaluate, implement, and extend ServiceNow AI Control Tower as an implementation environment for AI governance, connecting AI inventory, risk, compliance, approvals, workflows, and evidence with the broader ServiceNow ecosystem.
The platform provides governance capabilities.
The operating model, controls, and processes make them work.
WAYS TO START
FREE – 5 MINUTES
HALF DAY
2-4 WEEKS
60 DAYS
Start Where You Are
FREE – 5 MINUTES
AI Governance Readiness Assessment
Answer 16 questions and get an instant readiness report with a 90-day roadmap.
HALF DAY
Executive AI Governance Workshop
Align leaders on risk appetite, ownership, priorities, and the first governance decisions to make.
2-4 WEEKS
AI Governance Platform Review
Evaluate governance platforms, including ServiceNow AI Control Tower, against your requirements and stack.
60 DAYS
Agent Governance Pilot
Put identity, permissions, guardrails, and monitoring around one production AI agent.
Then grow into a full engagement
Step 1
Assess
Understand your AI environment, risks, governance gaps, and regulatory exposure.
Step 2
Build
Design and implement governance processes, controls, workflows, platforms, and oversight mechanisms.
Step 3
Operate
Continuously monitor AI systems, manage changes and exceptions, maintain evidence, and evolve governance as AI use expands.
FREE AI GOVERNANCE READINESS ASSESSMENT
How ready are you to
How ready are you to
scale AI agents?
12 questions. 4 minutes. An instant report showing where you stand and what to fix first.
Your report includes
- A snapshot of your AI inventory
- A risk-tier view of your use cases
- Gaps against NIST AI RMF, ISO/IEC 42001, and the EU AI Act
- A prioritized 90-day roadmap
THE V-SOFT DIFFERENCE
Governance that Works Beyond the Policy Page
Most AI governance programs can define policies and frameworks. The harder part is making them work across the data, identities, applications, agents, and cloud environments where AI operates. That is where V-Soft is strongest.
Our overall delivery record
110+
Cloud, Data & AI Implementations Delivered
Engineering-Led Execution
AI, data, security, cloud, application, and integration expertise in one team, backed by our Enterprise Integrations and Data & Analytics practices.
Control Engineering
Policies and risk requirements turned into controls that can be implemented, enforced, and tested, including controls on what AI agents can do.
Continuous Evidence
Assessments, approvals, controls, exceptions, monitoring, and remediation connected into traceable evidence.
Build Confidence into
Your AI Future
As AI evolves, your governance model should evolve with it, continuously adapting to new use cases, agents, data, regulations, and business priorities.
Frequently Asked Questions
AI governance services help an organization decide what its AI systems are allowed to do, and prove they stay within those limits. They cover the inventory of AI in use, risk classification, policies and controls, human oversight, testing, runtime monitoring, and audit-ready evidence, across models, agents, data, applications, and workflows.
Data governance manages the data itself. AI governance manages what AI does with that data: the decisions it makes, the actions it takes, and the risks it creates. The two increasingly need to run as one program.
If you have more than a handful of AI use cases, a platform can help you scale governance beyond spreadsheets and manual processes. It can automate AI inventory, intake, risk assessments, approvals, monitoring, and evidence collection. But a platform is only effective when supported by a clear governance operating model, defined ownership, and processes.
Treat each AI agent as a governed identity with defined permissions, autonomy limits, and human oversight requirements. Controls should be enforced as the agent acts, with monitoring and intervention built into the workflow, not applied only after the fact.
It can. The EU AI Act may apply to organizations outside the EU when they place AI systems or services on the EU market or when the output of those systems is used in the EU. The specific obligations depend on the organization’s role and use of AI. Confirm your obligations with legal counsel.
The AI Act introduces requirements that make AI classification, transparency, risk management, documentation, human oversight, monitoring, and accountability important governance considerations. Its provisions apply on different timelines, including transparency requirements from August 2, 2026, certain high-risk rules from December 2, 2027, and high-risk AI embedded in regulated products from August 2, 2028.
AI & Process Intelligence identifies where AI and process improvements can create value. AI Governance provides the controls to determine what AI can do, where it can operate, what risks it introduces, and how it is monitored.
| Discipline | What it governs | Typical owner | Example controls |
|---|---|---|---|
| AI governance | What AI systems can do, where they operate, the risk they create, and how they are monitored | AI council, Chief AI Officer, CISO, Risk, Legal | AI inventory, risk-tiered intake, approval gates, runtime guardrails, audit evidence |
| Data governance | The data itself: quality, lineage, classification, access, and retention. | Chief Data Officer, data owners and stewards | Data catalog, classification, access policies, retention rules |
| Responsible AI | The principles AI should follow: fairness, transparency, accountability, and safety | Executive leadership, ethics board | AI principles, impact assessments, bias testing, disclosure standards |
| AI TRiSM | Trust, risk, and security management of AI in the technology layer | CISO, AI and platform engineering | Model and application security, runtime inspection, data protection |
AI governance vs. data governance, responsible AI, and AI TRiSM
| Discipline | What it governs | Typical owner | Example controls |
|---|---|---|---|
| AI governance | What AI systems can do, where they operate, the risk they create, and how they are monitored | AI council, Chief AI Officer, CISO, Risk, Legal | AI inventory, risk-tiered intake, approval gates, runtime guardrails, audit evidence |
| Data governance | The data itself: quality, lineage, classification, access, and retention. | Chief Data Officer, data owners and stewards | Data catalog, classification, access policies, retention rules |
| Responsible AI | The principles AI should follow: fairness, transparency, accountability, and safety | Executive leadership, ethics board | AI principles, impact assessments, bias testing, disclosure standards |
| AI TRiSM | Trust, risk, and security management of AI in the technology layer | CISO, AI and platform engineering | Model and application security, runtime inspection, data protection |
ASSESSMENT