The AI Operating Layer for Control Assurance.
Manual processes, disconnected evidence, and spreadsheet-driven testing slow control assurance and limit visibility into emerging risk.
Control walkthroughs, evidence requests, testing, and reporting are coordinated manually, stretching assurance cycles across weeks.
Evidence is scattered across emails, shared drives, GRC platforms, audit repositories, and operational systems, making validation slow and inconsistent.
Different reviewers interpret controls differently, resulting in inconsistent testing decisions and uneven assurance quality.
Periodic testing leaves long gaps between review cycles, allowing operational and compliance risks to emerge unnoticed.
Experienced assurance professionals spend valuable time executing repetitive testing instead of focusing on oversight and risk analysis.
Leadership often waits until testing cycles are complete before receiving meaningful assurance insights and risk visibility.
ANA uses domain-trained agentic AI to automate the entire control testing lifecycle, from walkthroughs and evidence collection to validation, gap identification, and executive reporting. Built specifically for regulated enterprises, ANA combines intelligent automation with deterministic control logic, audit-ready traceability, and human-reviewed governance.
ANA uses domain-trained agentic AI to automate the entire control testing lifecycle, from walkthroughs and evidence collection to validation, gap identification, and executive reporting. Built specifically for regulated enterprises, ANA combines intelligent automation with deterministic control logic, audit-ready traceability, and human-reviewed governance.
ANA executes the entire control assurance lifecycle as a single orchestrated sequence.
Every stage is
observable, every decision is reproducible, every artifact is lineage-tracked.
Structured discovery.
Captures controls, ownership, and evidence for defensible documentation.
Evidence ingestion.
Ingests and normalises evidence from systems, repositories, and document uploads.
Control design validation.
Validates control design against identified risks, policies, regulatory requirements, and control objectives.
Evidence-based testing.
Tests operational evidence, highlighting exceptions requiring reviewer validation.
Gap detection.
Identifies control gaps across operational, compliance, fraud, and technology risks.
Audit-ready reporting.
Generates audit-ready reports with complete evidence traceability.
ANA is designed to augment assurance teams, not replace governance. Every execution stage includes reviewer visibility, escalation pathways, and traceable audit logic.
Human reviewers validate outputs, observations, and escalations before finalization.
Every testing decision and override remains reproducible under examiner scrutiny.
Source-to-conclusion evidence lineage maintained across all artefacts.
Testing methodology remains transparent, structured, and explainable.
Designed for regulatory trust, internal audit defensibility, and governance review.
Ambiguous evidence automatically routed for human validation.
A structural shift, from periodic, manual, fragmented control testing to continuous, traceable, AI-driven assurance execution.
Every use case is workflow-driven, audit-aligned, and tied to measurable operational pain. ANA isn't a generic AI overlay, it's an execution engine for specific assurance lifecycles.
Automate walkthroughs, execute control tests, validate evidence, and document results with AI-assisted accuracy.
Collect, organize, and validate evidence automatically from enterprise systems and business applications.
Monitor critical controls in real time, detect failures early, and surface emerging operational risks.
Accelerate planning, fieldwork, testing, workpapers, and reporting across the entire audit lifecycle.
Track findings, assign ownership, monitor remediation progress, and verify issue closure.
Generate audit-ready documentation and maintain continuous evidence for regulatory examinations.
Automate walkthroughs, execute control tests, validate evidence, and document results with AI-assisted accuracy.
Collect, organize, and validate evidence automatically from enterprise systems and business applications.
Monitor critical controls in real time, detect failures early, and surface emerging operational risks.
Accelerate planning, fieldwork, testing, workpapers, and reporting across the entire audit lifecycle.
Track findings, assign ownership, monitor remediation progress, and verify issue closure.
Generate audit-ready documentation and maintain continuous evidence for regulatory examinations.
ANA is deployed securely within your environment, maintaining data ownership, governance controls, and regulatory alignment. Evidence, documents, and assurance outputs remain inside the perimeter throughout the lifecycle.
Deploy ANA on-premises, within private cloud environments, or inside your tenant architecture.
Documents and evidence never leave your environment. Processing stays at the perimeter.
Every AI action, reviewer override, and assurance output is logged and reproducible.
Supports first-line execution, second-line validation, and third-line audit evaluation.
Encryption at rest and in transit, RBAC, audit logging, and governance policy alignment.
AWS, Azure, GCP, hybrid cloud, or private infrastructure support.
Direct answers to the questions that surface most often in conversations with CROs, Internal Audit, and Compliance leaders.
RPA executes deterministic tasks on structured inputs. ANA interprets unstructured evidence — including
policies, walkthroughs, narrative documentation, and operational artifacts — applies control logic, and
produces reasoned assurance outputs under reviewer governance.
Workflow platforms route work. ANA executes the testing itself.
RPA executes deterministic tasks on structured inputs. ANA interprets unstructured evidence — including policies, walkthroughs, narrative documentation, and operational artifacts — applies control logic, and produces reasoned assurance outputs under reviewer governance.
Workflow platforms route work. ANA executes the testing itself.
ANA is designed for examiner defensibility from the ground up. Every testing decision is reproducible, every override is logged, and every conclusion is backed by complete source-to-output evidence lineage.
The methodology is transparent, deterministic, and fully reviewable, not a black box. Examiners review the same evidence, rationale, and artifacts available to assurance teams.
Ambiguous or low-confidence evidence is automatically escalated to human reviewers through the Human-in-the-Loop governance layer — never silently resolved.
Reviewer interventions and overrides become first-class audit artifacts within the assurance trail. ANA is designed to surface uncertainty explicitly, not conceal it.
ANA supports all three lines of defense without collapsing governance boundaries.
The first line uses ANA for continuous control execution and monitoring. The second line operates against the same evidence base for independent validation and challenge. The third line (Internal Audit) inherits a complete, reproducible assurance artifact set for evaluation and examination readiness.
Governance separation remains fully intact.
No. ANA operates alongside existing GRC, audit, and risk platforms as the AI-native operating layer for continuous control assurance.
Existing platforms remain the systems of record, while ANA executes testing workflows, evaluates evidence, and contributes assurance outputs back into the enterprise governance stack.
Go deeper on the thinking, the architecture, and the operating model behind ANA.
A complete overview of ANA's architecture, lifecycle stages, governance posture, and deployment model.
Download PDFA practitioner's view of how agentic AI changes the operating model for risk and control functions.
Read white paperHow a regional bank modernised its RCSA cycle and expanded control coverage with ANA.
Read case studyA perspective on the structural reasons assurance functions struggle to keep pace.
Read blogSee what ANA can do in your control environment. Built for regulated enterprises, and run entirely inside your own environment.
