Responsible, scalable and reliable
Trust in AI
Businesses are thinking more about how to generate returns from AI as opposed to cutting costs. As AI takes on a greater role in customer interactions, operations and strategic decisions, businesses and leaders need assurance that AI systems are delivering outcomes that are responsible, reliable and aligned with organisational objectives. Yet, many face an AI trust gap: while expectations for AI continue to grow, concerns around accountability, reliability, security and oversight can slow adoption and limit value realisation.
Through our AI governance framework, AI assurance services, and practical risk-based approach, we help organisations balance AI advancement responsibly with effective governance, giving stakeholders greater confidence in AI-driven outcomes.
Recognised contributor to trusted AI
We are a testing partner in the Global AI Assurance Pilot led by IMDA and the AI Verify Foundation, supporting UOB and Standard Chartered Bank on these case studies.
We also contributed to Singapore IMDA's Model AI Governance Framework for Agentic AI, helping shape practical governance guidance for the next generation of AI systems.
Trust in AI: Our solutions
We bring you solutions at every stage of your AI lifecycle, helping you build effective governance, validate that your AI systems are performing as intended, and provide assurance that controls and risk management practices are working effectively.
Assess your AI governance maturity and build frameworks, accountability structures, policies and operating models aligned with your business objectives and regulatory expectations.
Outcomes:
- Clear lines of accountability
- Consistent AI oversight
- Scalable governance model
- Enhanced regulatory readiness
1
AI governance
Is your AI governance approach fit for purpose?
Independently assess your generative AI and agentic AI systems for performance, safety, security, robustness and operational controls.
Outcomes:
- Increased confidence in AI outputs and actions
- Clearer visibility of limitations and risks
- Stronger evidence for deployment decisions
- Greater stakeholder trust in AI outcomes
2
Agentic AI testing
Are your AI systems behaving as intended?
Provide independent assurance over AI governance, controls, and processes using recognised assurance standards (e.g. ISAE 3000), to report on the design, and operating effectiveness of controls prescribed by published frameworks such as ISO 42001, NIST, the EU AI Act, MOH AIHGle 2, MAS AIRG.
Outcomes:
- Independent validation
- Increased stakeholder confidence
- Demonstrable control effectiveness
- Stronger governance and compliance posture
3
Monitoring and assurance
Can you provide stakeholders with ongoing assurance over your AI governance?
Where to get started
Starting on the right foot is half the battle won. Whether you're beginning your AI journey or scaling enterprise adoption, these six building blocks provide a practical foundation for trusted AI.
- Assess your baseline
- Build foundational capabilities
- Design governance and operating model
- Embed trust by design
- Scale with confidence
- AI assurance
Assess your baseline
Understand your current AI governance maturity, priority risks and path forward
- AI governance, maturity and regulatory readiness assessment
- Gap assessment against applicable regulations, standards and industry guidelines
- Prioritised AI governance improvement roadmap
Build foundational capabilities
Establish the foundations for responsible AI adoption
- Responsible AI principles and enterprise AI policy
- AI use case and AI agent inventory and ownership
- AI risk appetite and risk taxonomy
- Risk-based intake, materiality assessment and tiering
Design governance and operating model
Define clear accountability, decision rights and oversight across the AI lifecycle
- Governance operating model, roles and responsibilities
- Governance forums, decision rights and escalation paths
- Risk and control ownership across the lines of defence
- Exception, issue and remediation governance
- Governance reporting and management information
- Training, communications and role-based capability building
Embed trust by design
Translate policy requirements into lifecycle standards, controls, testing and monitoring
- AI lifecycle standards, procedures and controls
- Data, model, security, privacy and human oversight controls
- Generative and agentic AI controls, including permissions, AI agent observability, action approval and safe intervention mechanisms
- Model and system testing, including agentic AI testing and red teaming
- Production monitoring, incident response and revalidation
- Remediation tracking and control improvement
Scale with confidence
Integrate AI risk management into existing enterprise functions as adoption expands
- Cybersecurity and technology risk
- Data governance and privacy
- Legal, compliance and regulatory change
- Third-party risk management
- Model risk management
- Enterprise risk and operational resilience
- Internal audit
- Workforce capability and change management
AI assurance
Independently assess AI systems, controls and governance to demonstrate trust
- Independent assurance, audit and attestation over AI governance and systems
- Independent technical validation of traditional, generative, agentic and multi-agent AI systems
- Assurance over the design and operating effectiveness of AI-related controls
- Readiness assessments against relevant frameworks, regulatory expectations and industry guidelines (e.g. ISO/IEC 42001:2023, EU AI Act, NIST, MOH AIHGle 2.0, MAS Proposed Guidelines on AI Risk Management)*
- Independent assurance reports using a recognised assurance standard (e.g. ISAE 3000), to report on the design and operating effectiveness of controls prescribed by published frameworks such as ISO 42001, NIST, EU AI Act, MOH AIHGle 2*, MAS AIRG*
*MOH AIHGle 2.0 = Singapore Ministry of Health Artificial Intelligence in Healthcare Guidelines; MAS Proposed Guidelines on AI Risk Management (MAS AIRG) = Monetary Authority of Singapore Proposed Guidelines on Artificial Intelligence Risk Management for Financial Institutions
Define your path to trust AI
Every organisation's AI journey is different, and we can help you identify practical priorities and the most relevant starting point for your organisation to build trust and confidence in AI.
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