AuditorAI brings structure, evidence, and control to modern IT audit.
AuditorAI turns IT audit into a clear, controlled, evidence-driven workflow with predefined control questions, document review against criteria, and outputs that are easier to defend in front of leadership, oversight bodies, and stakeholders.
Designed for teams that need audit discipline, evidence control, and defensible conclusions.
AuditorAI is built for structured audit work: scope definition, control review, evidence assessment, traceable findings, and defensible conclusions. The language, workflow, and outputs are shaped for real audit teams and real oversight environments.
Built around real audit methodology
AuditorAI reflects how serious audit work is actually performed: define scope, assess controls, collect evidence, review support, and defend conclusions with traceable logic.
AI that reviews evidence, not just stores it
AuditorAI helps analyse uploaded documents against audit questions, expected evidence, and COBIT-oriented criteria so teams can judge whether submissions truly support the control.
Portfolio visibility across many audits
Audit leadership gets one view of progress, weak controls, missing support, evidence quality, process themes, and reporting readiness across institutions and engagements.
Modern IT audit is inseparable from systems, controls, risk, and evidence.
Financial reporting, operational performance, compliance, and public accountability now depend on information systems. That is why modern audits increasingly require structured evaluation of IT-related controls, document content, evidence quality, and the impact of system weaknesses on audit objectives.
Threats
Identify what can go wrong across systems, processes, and information flows.
Vulnerabilities
Find where controls are weak, missing, outdated, or inconsistently applied.
Controls
Evaluate the measures designed to reduce risk and protect information.
Priority
Focus audit effort on the areas with the highest impact and exposure.
One platform that still respects the differences between audit mandates.
AuditorAI reflects the way IT components appear across financial, performance, and compliance audits. The platform helps teams structure work without flattening the real logic behind different review objectives.
Financial audit
Evaluate whether systems and controls support accurate, reliable, and accountable financial information.
Performance audit
Assess whether systems and processes operate economically, efficiently, and effectively in delivering intended outcomes.
Compliance audit
Verify alignment with laws, regulations, internal rules, and information governance requirements.
Clear methodology, clear criteria, clear outputs.
AuditorAI helps convert complex IT audit thinking into a more usable operational model. Teams can work with control questions, expected support, uploaded documents, reviewer comments, and audit conclusions in a way that is easier to repeat, supervise, and scale.
The platform is built on the COBIT methodology and includes more than 250 audit questions that systematically cover all IT governance, control, and risk domains. Artificial intelligence evaluates each response against thousands of criteria, meaning that the assessment goes beyond the mere existence of documents to examine content quality, control effectiveness, and actual performance in practice. This enables organizations to obtain an objective, consistent, and evidence-based evaluation, reduce audit time, and eliminate subjectivity. The platform's multilingual architecture ensures a uniform methodology across different markets, while the results provide a reliable foundation for decision-making, risk reduction, and the improvement of IT governance maturity.
Audit scope derived from mandate and risk
AuditorAI supports structured scoping so teams can move from audit purpose to the IT areas and controls that matter most.
General and application control thinking
Capture both foundational control conditions and application-level processing checks within one consistent review flow.
Clear criteria, evidence, and judgement
Questions, criteria, expected support, uploaded evidence, reviewer comments, and conclusions remain connected from start to finish.
Understandable reporting logic
Technical review work is transformed into audit-ready outputs that are easier for leadership, reviewers, and auditees to follow.
Purpose-built capabilities for a serious, evidence-led audit practice.
AuditorAI helps teams move faster without losing control of the methodology. It combines structured control libraries, uploaded document review, AI assistance, and traceable reporting logic in one platform.
Predefined IT general control question sets
Start faster with structured control questions aligned to real audit practice instead of building everything manually from scratch.
AI-powered review of uploaded documents
AuditorAI analyses uploaded documents against control criteria and expected evidence, helping reviewers determine whether the submission truly supports the answer.
Criteria-based evidence assessment
Go beyond attachment collection. Review evidence content against the question, the criterion, and the control logic behind the audit step.
Control-to-evidence traceability
Every answer, upload, observation, and conclusion can be followed through one connected audit trail.
COBIT-aligned roll-up logic
Aggregate question-level work into broader governance and management views for clearer reporting and prioritisation.
Controlled AI in a reviewer-led process
AI accelerates analysis and structuring, while final professional judgement remains with the auditor and review team.
A controlled workflow from audit questions to evidence-based conclusions.
AuditorAI helps reviewers move from evidence request to judgement without losing the story of the audit.
Start with predefined IT general control questions and align the engagement to the audit scope, entity, and review objectives.
Audited organisations respond through a controlled portal and provide policies, procedures, records, logs, reports, plans, and other supporting artefacts.
AuditorAI helps reviewers analyse uploaded documents against the question criteria and expected support, identify gaps, and structure observations while keeping the reviewer in control.
Move from control-level assessment to findings, capability signals, reporting inputs, and defensible audit conclusions.
Controlled entity workspace
Each audited organisation sees only its own audit perimeter, responses, evidence requests, reviewer feedback, and history.
AI-assisted document-to-criteria analysis
AuditorAI helps teams review whether uploaded documents actually support the question and criterion, not merely whether a file was attached.
Evidence-to-conclusion linkage
Documents remain linked to criteria, observations, control assessments, and conclusion logic so the trail stays defensible.
Outputs built for reporting
The workflow supports audit-ready communication instead of leaving teams to reconstruct the logic manually at the end.
Built for audit teams working at institution scale.
AuditorAI is intended for organisations that need more than a portal. It is built for teams that care about audit consistency, evidence discipline, methodology control, and scalable oversight.
Supreme audit institutions
Support institution-level and cross-institution IT assurance work with more standardised control language, stronger evidence discipline, and more comparable outputs.
External audit and assurance firms
Scale delivery across multiple clients and engagements while preserving methodology, reviewer accountability, and partner-level oversight.
Internal audit and governance teams
Modernise recurring IT general controls, governance, continuity, cybersecurity, and compliance assessments in one controlled workspace.
Trust in audit outputs starts with trust in the operating model.
AuditorAI helps preserve evidence integrity, role clarity, review traceability, and leadership oversight across the full engagement lifecycle.
Role-based access
Separate auditors, auditees, reviewers, and decision-makers through controlled permissions and accountability lines.
Entity isolation
Each organisation works within its own audit perimeter, evidence scope, and review context to preserve confidentiality.
Evidence integrity
Preserve uploaded support, metadata, history, reviewer actions, and finding context across the audit lifecycle.
End-to-end traceability
Track requests, answers, observations, adequacy assessments, and conclusion logic in one connected workflow.
Leadership transparency
Give audit leadership a portfolio-level view of progress, risk posture, missing support, and reporting readiness.
Controlled AI assistance
Accelerate review and structuring with AI while keeping professional responsibility in human hands.
AuditorAI gives audit teams a board they can actually work from.
AuditorAI combines structured questions, controlled evidence collection, AI-supported review, and traceable conclusion logic in one operational workspace for serious IT audit delivery.
Built around audit logic, controls, evidence, and reviewer accountability.
Structured around support quality, traceability, and conclusion defensibility.
Designed for many entities, many reviewers, and many concurrent audits.