AI Governance & Readiness
Accelerate AI Adoption. Govern Risk. Protect Your Data.
AI is transforming how organizations operate, make decisions and engage with customers. But scaling AI safely requires more than selecting the right models and platforms. Organizations need the right governance, data foundations, security controls, architecture and accountability to adopt AI with confidence.
Turn AI Ambition into Controlled, Scalable Adoption
AI initiatives can move faster than the governance and security controls designed to manage them. Sensitive data may be exposed to AI platforms. New models and applications introduce additional attack surfaces. Responsibilities can be unclear. Third-party AI services create new dependencies. Existing security controls may not adequately address AI-specific risks.
Our assessment helps answer five critical questions:
Do we have effective AI governance?
Establish clear ownership, policies, oversight and risk accountability.
Is our data ready for AI?
Understand whether data quality, classification, ownership and governance can support intended AI use cases.
Are our security controls sufficient?
Evaluate whether sensitive information remains appropriately protected throughout the AI lifecycle.
Is our architecture ready?
Assess network connectivity, trust boundaries, data flows, integrations and segmentation.
Can we demonstrate responsible AI?
Evaluate transparency, accountability, human oversight, privacy, security and responsible-use mechanisms.
Our Assessment
AI Governance & Readiness Assessment
AI Governance
Build governance that enables AI rather than slowing it down.
Effective AI governance creates clear guardrails for innovation while ensuring risks are understood, owned and managed.
Outcome
A clear governance model establishing ownership, accountability and appropriate controls across the AI lifecycle.
We evaluate
- AI governance structures
- Policies and standards
- Roles and accountability
- AI use-case approval
- Risk assessment processes
- Model and application ownership
- AI lifecycle governance
- Third-party AI oversight
- Exception management
- Monitoring and reporting
- Regulatory and policy alignment
- Executive and board-level oversight
Data Readiness
Make sure your data is ready before your AI depends on it.
The quality and governance of data directly influence the effectiveness, security and reliability of AI systems.
Outcome
A data readiness baseline identifying the improvements required to support secure, scalable AI adoption.
We evaluate
- Data Quality — accuracy, completeness, consistency and suitability for intended AI use cases
- Data Classification — identification and handling of confidential, sensitive, personal and regulated information
- Data Ownership — clear accountability for datasets used to develop, train, augment or operate AI solutions
- Data Provenance — understanding where data originates, how it has been transformed and whether it is appropriate for its intended use
- Data Governance — policies, controls and lifecycle processes governing access, use, sharing, retention and disposal
Data Security Control Evaluation
Protect sensitive information throughout the AI lifecycle.
AI can create new pathways for sensitive information to leave established security boundaries. We evaluate whether existing security controls adequately address AI-specific data risks.
Outcome
A prioritized view of data security control gaps and practical recommendations for protecting information used by AI.
We evaluate
- Identity and access management
- Privileged access
- Encryption and key management
- Data loss prevention
- Data masking and tokenization
- Secrets management
- Logging and monitoring
- Data access controls
- Information lifecycle management
- AI application integrations
- Model and API access
- Third-party AI platforms
- Sensitive-data exposure
- Data retention and deletion
AI Network & Security Architecture
Build secure foundations for enterprise AI.
AI changes enterprise data flows and introduces new connections between users, applications, models, cloud platforms and external services.
Outcome
A security architecture view identifying unnecessary exposure, weak trust boundaries and opportunities to strengthen AI infrastructure.
We evaluate
- Connectivity — how AI platforms, applications, models and data sources communicate
- Trust Boundaries — where information crosses security, organizational or technology boundaries
- Segmentation — whether AI environments, workloads and sensitive data are appropriately isolated
- Data Flows — how information moves into, through and out of AI services
- API Security — controls protecting model endpoints, integrations and machine-to-machine communications
- Cloud & Hybrid Architecture — security implications across cloud, on-premises and hybrid AI environments
- Third-Party Connectivity — connections to external models, SaaS platforms, AI services and technology providers
Responsible AI
Turn responsible AI principles into operational controls.
Responsible AI requires more than policy statements. Organizations need governance mechanisms that translate principles into practical controls throughout the AI lifecycle.
Outcome
A responsible AI control framework aligned to the organization's risk profile and AI ambitions.
We evaluate
- Transparency — understanding and communicating how AI is being used
- Accountability — establishing clear ownership for AI systems, decisions and outcomes
- Human Oversight — determining where human review, intervention and decision authority are required
- Privacy — protecting personal and sensitive information throughout AI processing
- Security — managing threats affecting AI models, applications, infrastructure and data
- Responsible Use — defining acceptable use and appropriate safeguards for employees, developers and business functions
- Monitoring — establishing mechanisms to identify emerging risks, inappropriate use and control failures
AI Readiness Across the Enterprise
Assess the Full AI Ecosystem
Our approach considers AI as an interconnected ecosystem rather than assessing individual models in isolation.
People
Roles, responsibilities, skills, accountability and human oversight.
Governance
Policies, standards, decision rights, risk management and oversight.
Data
Quality, classification, ownership, provenance, privacy and protection.
Models
Model lifecycle, access, security, validation and monitoring.
Applications
AI-enabled applications, copilots, agents, APIs and integrations.
Infrastructure
Cloud, compute, platforms, networks and supporting technology.
Third Parties
AI vendors, foundation-model providers, SaaS platforms and external services.
From AI Experimentation to Enterprise Readiness
A Practical Path to Secure AI Adoption
Discover
Understand existing AI use cases, platforms, models, data and dependencies.
Assess
Evaluate governance, data, security controls, architecture and responsible AI practices.
Identify Risk
Determine material security, data, governance and operational gaps.
Prioritize
Focus remediation on the risks that matter most to AI adoption and business objectives.
Establish Guardrails
Define governance, architecture, security controls and responsible-use requirements.
Scale
Enable business teams to deploy AI within clearly defined security and governance boundaries.
Govern
Continuously monitor AI adoption, risk, controls and emerging requirements.
What You Receive
More Than an Assessment — A Roadmap for Action
The engagement translates findings into practical outputs that can support your AI programme, security leadership and executive decision-making.
Executive AI Readiness Assessment
A concise view of current maturity, material risks and priority actions.
AI Governance Assessment
Evaluation of policies, accountability, oversight and lifecycle governance.
Data Readiness Assessment
Analysis of data quality, classification, ownership, provenance and governance.
Security Control Assessment
Evaluation of controls protecting data, models, AI applications and supporting infrastructure.
AI Architecture Assessment
Review of connectivity, segmentation, trust boundaries, APIs and information flows.
Responsible AI Assessment
Evaluation of accountability, transparency, privacy, security and human oversight.
Risk & Gap Heatmap
Prioritized findings based on business impact, likelihood and AI adoption objectives.
AI Readiness Roadmap
A sequenced plan for strengthening governance, security, architecture and data foundations.
AI Readiness at a Glance
Assessment Area
Key Question
AI Governance
Do we have clear ownership, policies and oversight?
Data Readiness
Is our data suitable and governed for AI?
Data Security
Is sensitive information appropriately protected?
Architecture
Are AI platforms and integrations securely designed?
Network Security
Are trust boundaries and data flows appropriately controlled?
Responsible AI
Can we demonstrate accountable and responsible AI use?
Third Parties
Do we understand risks introduced by external AI services?
Operations
Can controls scale as AI adoption increases?
Roadmap
Do we know what needs to change and in what order?
Related Services
Cyber Security Assessment
Baseline your enterprise security posture alongside your AI governance programme.
Zero Trust Architecture
Extend zero trust controls to AI agents, machine identities, and data flows.
Post-Quantum Cryptography
Protect AI model data and long-lived assets from quantum computing threats.
Ready to Assess Your AI Readiness?
Get an independent view of your organization's ability to deploy and scale AI securely and responsibly.