DataSnipper’s new AI Agents — Disclosure Agents and Excel Agents — arrive as a pragmatic, Microsoft-powered push to bring
agentic AI directly into the audit room, promising faster disclosure reviews, end-to-end Excel-native testing, and audit-ready, traceable outputs that aim to reduce manual work without replacing human judgment.
Background
DataSnipper, the Amsterdam-born intelligent automation platform for audit and finance teams, has been steadily expanding its AI footprint since a $100 million Series B round led by Index Ventures repositioned the company as a European unicorn. The firm has emphasized deep integration with Microsoft’s cloud ecosystem: DataSnipper is listed on the Azure Marketplace and positions its agent products as Azure-powered, enterprise-ready tools for regulated environments. The company’s October launch frames the new Agents as a response to two stubborn pain points in audit workflows: slow, manual disclosure checklist reviews against IFRS/GAAP and the heavy reliance on Excel for test execution and evidence linking. DataSnipper says the Agents were built to sit inside the tools auditors already use — primarily Excel — and to produce outputs that are explainable and traceable for regulatory review.
Overview: What are Disclosure Agents and Excel Agents?
Disclosure Agents — automating disclosure reviews with traceable evidence
Disclosure Agents are designed to automate and accelerate disclosure checklist reviews. The agent can scan a disclosure checklist and the associated financial statements, identify missing or misaligned disclosures against IFRS and GAAP frameworks (and firm-specific checklists), and link each requirement back to precise evidence in the underlying documents. The stated output is a side-by-side visualization of requirement versus evidence, with a full audit trail. Key advertised capabilities:
- Automatic analysis of disclosure checklists against financial statements.
- Cross-checking against IFRS, GAAP, and custom firm policies or templates.
- Evidence linking that produces a clear audit trail for reviewers and regulators.
Excel Agents — agentic automation inside the spreadsheet
Excel Agents are
Excel-native agents that automate common audit testing steps end-to-end within the workbook environment auditors already use. The product positions itself as a prompt-driven workflow engine inside Excel that will:
- Match sample data to source documents,
- Extract key fields,
- Compare results to expectations,
- Produce explainable, cross-referenced, audit-ready outputs — all without complex templates or pre-training on customer data.
DataSnipper describes Excel Agents as keeping the auditor “in the loop”: the agent performs heavy lifting while the professional retains judgment, sign-off, and final control.
The Microsoft and Azure angle: why it matters
DataSnipper frames the Agents as
Azure-powered and has formally listed the platform on the Azure Marketplace. That placement offers easier procurement for Microsoft-centric enterprises and signals a technical alignment with Azure services for scalability, compliance tooling, and enterprise support channels. This timing aligns with a broader shift in the industry towards agentic AI across clouds — Microsoft has been building agent support into Azure AI Foundry and Copilot tooling, and has publicly signaled support for interoperable agent protocols. That ecosystem-level momentum matters: auditors and finance teams prefer solutions that integrate with their cloud strategy and corporate governance frameworks.
Technical and compliance claims: what DataSnipper says
DataSnipper’s product pages and press materials make several specific technical and governance claims:
- The Agents run on Azure infrastructure and are positioned as enterprise-grade.
- The platform is SOC 2 Type II compliant, uses encryption in transit and at rest, and states that it does not fine-tune models on customer data. Prompts and documents are retained for up to 24 hours and then deleted, according to the vendor.
- Outputs are described as explainable and audit-ready, with every action traceable back to source materials.
These controls are central to selling AI into regulated audits: auditors, firms, and regulators must be able to trace how conclusions were reached, who approved them, and what evidence supports each assertion.
Why audit teams will care (and who will adopt first)
Audit and finance professionals face a unique combination of challenges: high volumes of unstructured documents, tight delivery deadlines, and stringent regulatory frameworks like IFRS and US GAAP. Traditional automation helps with repetition but falters where judgment, context, and cross-referencing are required.
DataSnipper’s Agents target these exact pressure points:
- External audit teams handling annual reports, group consolidations, and disclosure checklists.
- Internal audit and SOX teams who need rapid evidence collection and control testing.
- Corporate finance teams preparing statutory and regulatory reports across multiple accounting frameworks.
The vendor argues that Disclosure Agents can convert days of manual disclosure review into minutes and that Excel Agents remove repetitive copy/paste testing tasks so auditors can focus on risk and insights. These are positioned as productivity and quality improvements rather than replacements for professional judgment.
Early reception and industry context
DataSnipper’s agent launch was announced broadly via PR channels and highlighted during the company’s 2025 customer events, where the CEO framed the move as the start of the “agentic era” in audit. The same period saw the company included in industry lists as an innovation standout, reinforcing momentum behind audit-focused AI tooling. At the same time, Microsoft and other cloud vendors have been publicly advancing agent frameworks and governance tooling — an environment that helps vendors like DataSnipper deliver agent-based features into enterprise workflows with existing identity, compliance, and procurement channels.
Critical analysis: strengths
- Product-market fit: Excel is still the lingua franca of audit. Embedding agentic automation inside Excel lowers the friction for adoption and aligns with real, daily auditor workflows — a strong strategic advantage.
- Audit-readiness and evidence linking: The emphasis on traceable, cross-referenced outputs addresses a core barrier for generative AI in regulated work — the need for reproducible evidence and a clear audit trail. If the agent truly links every conclusion to a document location, that’s a major utility for reviewers and regulators.
- Microsoft/Azure alignment and marketplace presence: Listing on the Azure Marketplace simplifies procurement and signals enterprise-level cloud integration, resilience, and regional deployment options for customers with strict data residency needs.
- Governance-forward messaging: SOC 2 Type II compliance, encryption, and the vendor’s statement that models are not fine-tuned on customer data are credible risk-mitigation steps that enterprises will scrutinize closely. These claims reduce a key barrier to adoption.
- Momentum and credibility: The company’s funding, rapid user growth claims, and third-party recognition create favorable optics for firms evaluating procurement. The combination of VC backing and marketplace presence will make pilots easier to greenlight.
Critical analysis: risks, caveats, and unknowns
While the product vision is compelling, several substantive risks and limitations require attention.
- Model behavior and hallucination risk: Even with evidence-linking, agent outputs that synthesize judgment remain vulnerable to hallucinations or incorrect inferences. The claim that every result links back to its source reduces but does not eliminate the risk that an agent could misinterpret context or apply the wrong disclosure criterion. This remains principally an operator-and-GRC (governance, risk, compliance) problem.
- Vendor claims vs. independently verified performance: Time savings estimates, assertions that disclosure reviews can be reduced “from days to minutes,” and metrics like “automate away up to 90% of menial tasks” are vendor claims. Independent, third-party validation of error rates and audit-quality outcomes will be essential before firms expand production use. Treat these numbers as marketing until third-party audits or peer-reviewed case studies appear.
- Regulatory and professional liability: Audit conclusions remain the responsibility of licensed professionals and audit firms. Introducing agents that produce suggested testing or disclosure assessments raises questions of professional skepticism, supervisory review, and potential liability if an agent misses or misclassifies a material disclosure. Firms must update methodologies and supervision protocols to reflect agent-assisted work.
- Data residency and retention nuance: DataSnipper states short retention windows (e.g., prompts/docs retained up to 24 hours) and that the platform does not fine-tune on customer data. Buyers must validate where models run, whether any telemetry is stored globally, and how that aligns with regulators in different jurisdictions (EU, UK, US, APAC). Contractual SLAs and security attestations should be confirmed during procurement.
- Vendor lock-in and workflow entrenchment: The benefit of Excel-native agents is also a double-edged sword: deep Excel integration can create workflow lock-in that makes it costly to switch vendors, especially when agent outputs and evidence structures become embedded in a firm’s audit methodology. Firms should evaluate interoperability and export capabilities.
- Governance maturity inside audit teams: Many audit teams still lack formal AI governance, model validation pipelines, and adequate training for professionals to review agent outputs effectively. Governance maturity must grow in parallel with technology adoption.
Practical guidance for procurement and pilots
Firms considering Disclosure Agents or Excel Agents should move deliberately. The following checklist balances opportunity with prudent risk controls.
Pre-pilot checklist
- Define success metrics: error reduction targets, time saved per disclosure/test, and quality thresholds that preserve professional skepticism.
- Map data flows: confirm where data is stored, processed, and logged; require region-specific hosting if needed.
- Verify compliance artifacts: SOC 2 Type II report, penetration test results, encryption standards, and contractual data protection clauses.
- Insist on exportability: ensure evidence structures and output artifacts can be exported to firm repositories and existing workflows.
Pilot execution (recommended 8–12 weeks)
- Select representative engagements: choose files that include complex disclosures, multi-subsidiary consolidations, and high sample volumes.
- Run agent-assisted vs. manual parallel testing: compare outputs, time-to-completion, and error rates.
- Capture supervision time: measure the time required for reviewer oversight and validation.
- Record edge cases: catalog the situations where the agent failed, mislinked, or required human correction.
Governance and roll-out
- Update methodology manuals: explicitly define agent-assisted steps, required reviewer checks, and documentation practices.
- Train reviewers: run hands-on sessions so senior auditors know how to interrogate agent outputs and evidence links.
- Continuous validation: schedule periodic revalidation of the agent’s outputs and hold post-implementation audits to measure drift or degradation.
Implementation considerations for IT and security teams
- Identity and access management: integrate the agent into the firm’s SSO, role-based access, and approval workflows.
- Data retention and e-discovery: confirm policy settings for prompt and document retention and how deletion is audited.
- Logging and SIEM integration: ensure agent actions are forwarded to existing security monitoring tools for anomaly detection.
- Disaster recovery and availability: review Azure-region failover plans and SLAs in the vendor contract.
How regulators and professional bodies may respond
Regulators will watch agentic tools closely. The audit profession’s standard-setters emphasize documentation, evidence, and auditor responsibility; tools that can provide granular traceability are better aligned with those expectations.
However, formal guidance will likely require:
- Explicit documentation of the role the agent played,
- Sign-off controls by licensed auditors,
- Evidence that agents were validated and supervised,
- Clear records for regulator inspection.
Until professional bodies issue detailed guidance on AI-assisted audit workflows, firms must treat agent outputs as
assistive evidence requiring human sign-off and maintain conservative supervisory practices.
Market positioning and competitive landscape
DataSnipper’s strategy doubles down on a classic enterprise play: solve a high-value, high-frequency pain inside the core workflows auditors already use. Listing on the Azure Marketplace and emphasizing SOC 2, encryption, and non-fine-tuning are deliberate moves to reduce procurement friction and enterprise risk concerns.
Competing vendors will pursue similar agentic or generative features; the differentiators to watch will be:
- Depth of Excel integration and speed of deployment,
- Strength of evidence linking and explainability,
- Independent validation of accuracy and error rates,
- Commercial terms for regional deployment, data retention, and liability.
Firms should evaluate alternatives not just on feature parity but on verifiable performance and governance maturity.
Unverifiable or marketing-driven claims to treat cautiously
Several performance and scale claims in vendor materials should be regarded as
company-reported until independently validated:
- Exact time-savings claims such as “days into minutes” will vary by engagement complexity, and should be tested empirically in a pilot.
- Usage numbers (users or seats served) and efficiency percentages are useful signals of momentum but are not substitutes for third-party performance studies. These appear in press materials and investor posts and require independent verification.
When a vendor says “AI that knows IFRS and GAAP,” interpret that as: the product encodes rules, templates, and patterns relevant to common disclosure frameworks and can apply them in many cases. It is not a substitute for a standards expert; rather, it is a tool to accelerate the expert’s work.
Final take: what the launch means for auditors and IT leaders
DataSnipper’s Disclosure Agents and Excel Agents represent a significant step in operationalizing agentic AI for regulated, evidence-heavy workflows. The product solves a real friction point — the friction of repetitive, document-heavy audit tasks performed inside Excel — and takes the sensible approach of coupling automation with explainability and enterprise controls.
For audit firms and finance teams, the practical upside is compelling: faster disclosure reviews, more consistent evidence linking, and potential headcount redeployment to higher-value work. For IT and risk leaders, the priorities are robust vendor due diligence, clear data residency and retention terms, and a governance framework that preserves professional judgment and regulatory compliance.
The tools are ready for pilots today, but caution is warranted. Teams must validate vendor claims in their own context, document supervisory controls, and commit to ongoing validation. Properly governed, agentic AI inside Excel can be a productivity multiplier; without governance it becomes another opaque tool that raises more questions than it answers.
In short: DataSnipper’s Microsoft-powered Agents are a practical, cloud-aligned advance for audit automation that prioritize traceability and familiar workflows — powerful if governed well, and potentially risky if deployed without rigorous validation, supervision, and contractual protections.
Source: Silicon Canals
AI Tool of the Week: Amsterdam unicorn DataSnipper unveils Microsoft-powered AI Agents for faster, error-free audits - Silicon Canals