Trust across the
AI lifecycle.
From the first assessment to continuous assurance, each practice is built to produce clear decisions and defensible outcomes rather than another policy deck.
Advisory & Governance
Readiness, strategy and the accountability structure that has to exist before AI scales.
- AI readiness and maturity assessment across data, people, controls and use cases
- AI governance policy, acceptable use policy and roles matrix with RACI clarity
- Regulatory mapping for Malaysia’s National Guidelines on AI Governance & Ethics, PDPA obligations and sector rules
- ISO/IEC 42001 and NIST AI RMF gap analysis and certification-readiness roadmap
- AI inventory build: systems, vendors, owners, data flows and shadow AI discovery
Assurance & Security
Independent technical proof that a model behaves the way leadership claims it does.
- LLM evaluation: accuracy, robustness, refusal behaviour, hallucination and bias testing
- AI red teaming against prompt injection, jailbreaks and data-leakage paths
- Agentic AI review: tool permissions, autonomy limits, human-in-the-loop checkpoints
- Application and data security review of AI-enabled products and integrations
- Third-party and vendor AI assurance before procurement sign-off
Digital Solutions
Governance-by-design build work, so remediation and delivery sit with the same team.
- AI-enabled websites, portals and internal applications
- Retrieval-augmented generation systems over controlled corporate knowledge
- Copilots and workflow automation with logging, approval and audit trails built in
- Integration and data pipeline work that keeps sensitive data inside agreed boundaries
- Monitoring dashboards for AI usage, incidents and control status
Academy & Capacity Building
Capability that stays in the organisation after the engagement closes.
- Board and executive briefings on AI accountability and disclosure
- Practitioner workshops for risk, compliance, audit, legal and technology teams
- AI acceptable-use enablement for the wider workforce
- Certification-readiness programmes aligned to recognised AI governance standards
- AI Trust Clinic roundtables and sector-specific sessions
Entry, transformation,
then recurring assurance.
A short diagnostic that shows where AI is already in use and where exposure sits.
Paid assessment of maturity, data, controls and priority use cases.
Policies, ownership, risk assessment and control framework put in place.
Technical evaluation and red teaming to prove behaviour under pressure.
Implementation and engineering work to close the gaps found.
Evidence pack, management review and board-ready attestation.
Continuous monitoring, periodic review and refreshed testing.
First engagements are typically fixed-scope and fixed-fee, with retainer options for monitoring, periodic review and re-testing as AI use changes.
See the six-phase framework