AI Audit

An AI system that produces inaccurate outputs with confidence is more dangerous than one that produces none, because the inaccuracy is invisible. We evaluate whether a deployed system performs as specified: whether outputs are accurate, whether performance holds across the full range of inputs, and whether it degrades as data and conditions change. The methodology combines model performance evaluation, review of monitoring infrastructure, testing against edge cases and adversarial inputs, and assessment of the human oversight designed to catch failures. [Track: audit OF AI]

Decisions AI makes at scale - on credit, on hiring, on pricing - can discriminate systematically along lines the organization never intended and cannot see without independent evaluation. We examine decision logic for bias across protected and proxy characteristics, assess whether decisions can be explained to the parties they affect, and evaluate whether human oversight is proportionate to consequence. Findings identify both the presence of bias and its source, so remediation happens at the right level. [Track: audit OF AI]

Every AI system is as reliable as the data it was trained on and continues to receive. We examine the data assets AI depends on across four dimensions - quality, integrity, completeness and lineage - and produce a data quality score, identified integrity risks, gaps in coverage, and a lineage map documenting where data came from and how it was processed before reaching the system. [Track: audit OF AI]

AI systems in the UAE are subject to a growing body of obligations: Dubai AI Seal standards, the UAE Personal Data Protection Law, DIFC Data Protection regulations, sector-specific requirements in financial services and healthcare, and the principles of the UAE National AI Strategy 2031. We map each system against its applicable obligations and produce a gap analysis with documented evidence of conformance and non-conformance, structured for both internal governance and regulatory or certification submissions. [Track: audit OF AI]

AI introduces vulnerabilities traditional cybersecurity frameworks were not designed to detect. Adversarial inputs engineered to manipulate outputs can be invisible to standard monitoring. Data poisoning during training compromises a system long before deployment. Model theft exposes proprietary IP through a deployed system's outputs. We assess the attack surface, controls for detecting adversarial inputs, training pipeline security, access controls around model weights and training data, and monitoring for anomalous behaviour in production. [Track: audit OF AI]

Most organizations use more AI than they built. The AI embedded in a CRM, HR platform, financial reporting system and productivity suite is a portfolio of dependencies accepted without independent evaluation - and when those tools produce decisions affecting customers, employees or financial outcomes, accountability sits with the organization that deployed them, not the vendor. We evaluate externally sourced AI on accuracy, bias, data handling, security posture, vendor governance and contractual accountability, and produce an overall risk rating for the portfolio. [Track: audit OF AI]

For organizations wanting permanent internal capability rather than external engagements, we design and establish the function: the methodology, the tooling and data access, the training to build AI audit competence, the governance structure that gives the function independence, and the reporting lines connecting findings to the board. The engagement produces an operational function, including a first cohort of internal audits completed under supervision to validate the methodology before the team operates independently. [Track: audit OF AI]

Traditional sampling rests on statistical principles designed for an era when complete population testing was impractical. AI makes it feasible in many contexts, and where it is not, enables risk-stratified sampling that concentrates coverage on the transactions statistical approaches would find only by chance. We configure the tools, validate outputs against manual benchmarks, train the team to interpret AI-surfaced results, and build the documentation trail that keeps AI-assisted sampling within professional audit standards. [Track: AI IN auditing]

Point-in-time audits find what was wrong at the moment they looked. Continuous monitoring finds what is wrong as it happens. We design and implement monitoring frameworks using AI anomaly detection: the data sources monitored, the alert thresholds that separate genuine anomalies from operational noise, the workflow for investigating and escalating, and the reporting that keeps audit leadership informed without creating alert fatigue. [Track: AI IN auditing]

Document review is among the highest-volume and lowest-leverage activities in audit work. We implement AI document analysis in audit workflows - selecting tools for the document types and review tasks involved, configuring them for the organization's formats and objectives, validating extraction accuracy against manual benchmarks, and training the team to treat output as evidence rather than as a substitute for professional judgment. [Track: AI IN auditing]

Internal audit planning is only as good as the risk assessment driving it, and assessments resting on management interviews and last year's plan miss the emerging risks that matter most. AI processes a broader range of signals - transaction data, control test results, external risk indicators, operational metrics, exception reports - to surface patterns structured interviews would not reveal. We integrate this into the annual planning cycle, producing an audit universe and risk ranking the team can defend to the audit committee. [Track: AI IN auditing]

Financial AI systems - credit decisioning, fraud detection, forecasting, expense analysis, regulatory reporting - introduce complexities general-purpose AI audit methodology does not fully address. This specialization applies the principles of financial audit (materiality, evidence, professional skepticism) to AI systems in financial contexts, and applies AI audit methodology (model evaluation, bias assessment, data lineage) to the specific characteristics of financial data and decisions. [Track: both]