THE GLYNT GUIDE
ISSA 5000 and Audit-Ready Sustainability Data Standards for 2026
Audit standards in 2026 expect sustainability data that can be verified as ‘decision-useful’ and a body of evidence that can be tested. This guide covers the new ISSA 5000, ISO, and other standards, and shows how GLYNT.AI’s automated system supports them.
Key takeaways
New regulations are coming
Audits and rigorous verification are becoming a required part of sustainability data preparation. This includes upcoming regulations in California and requirements added by global supply chains such as Catena-X, Carbon Measures and PACT.
Spreadsheets won’t cut it
Audit-ready sustainability data can’t be prepared on a spreadsheet. It takes a complete system with both data-level and system-level tests. Auditors don’t just review a handful of numbers; they examine the underlying method and how it was implemented.
Automate to get audit-ready
An automated, purpose-built system is needed to deliver audit-ready sustainability data. GLYNT.AI is built specifically for this purpose, preparing sustainability data with the same rigor applied to financial data. Fully automated, GLYNT.AI meets the most demanding sustainability audit standards.
Introduction
Sustainability managers face the challenge of increasing demands for audited sustainability data, and the complexity of implementing a brand-new workflow and system. This is even more difficult because sustainability data is scattered across business systems, around the world and is incredibly varied and messy. It is tempting to focus on accuracy — which alone is a quite difficult challenge.
But auditors check the method of data preparation, and they want to see that your data aligns with the five key principles of data quality from the GHG Protocol. From the Protocol, their framework of reference is whether your data is: Relevant to users; Complete within the reporting boundaries; Consistent over time; Transparently presented; and Accurate.
This guide helps sustainability managers align with the GHG Protocol’s data quality principles, meet auditor standards, and integrate new workflows and systems into existing practices and software. It provides context, perspective, and detailed requirements.
GLYNT.AI is the foundational sustainability data layer — it sits underneath your reporting software and internal dashboards, lifting the burden of preparing accurate, audit-ready sustainability data. We built GLYNT.AI. We’re audited each year. This guide explains what it takes to build a system like it.
The 5 key principles of data quality for sustainability data
The following cards list the key data quality principles from the GHG Protocols. While this standard is the foundation for all other climate and sustainability reporting standards, it is not an operational recipe. Use the list to compare your current or proposed system to what is expected by the Protocols.
Relevance
Meet decision-makers’ needs.
Completeness
Documented boundary, reliable exclusion, complete inclusion.
Consistency
Data should be comparable over time.
Transparency
Clear and accessible audit trails and documentation. Assumptions noted.
Accuracy
Verification of accurate data, including reduction of uncertainty around prepared data.
What are the sustainability data audit standards for 2026?
The sustainability data audit standards in 2026 are in flux. The market is aligning around the use of IAASB ISSA 5000,and ISO and the GHG Protocol have announced a strategic partnership to form a single set of standards.
IAASB ISSA 5000
General Requirements for Sustainability Assurance Engagements. Issued by the International Auditing and Assurance Standards Board, this is the first global, cross-sector standard for sustainability assurance itself — the rules an assurance provider follows when reviewing any sustainability disclosure, regardless of the reporting framework. It covers limited and reasonable assurance ffor reporting periods beginning on or after December 15, 2026
ISO 14064-1 and 14064-2: GHG quantification and reporting
These define how an organization or project quantifies and reports greenhouse gas emissions, i.e. the accounting rules your data has to satisfy before anyone verifies it. ISO 14064-3 is the companion verification methodology, i.e. the actual procedures a verifier follows to test results, including how the evidence was gathered and the materiality assessment.
PACT Methodology
PACT (WBCSD’s Partnership for Carbon Transparency) governs a specific data area: how companies calculate and exchange product-level Scope 3 emissions data with suppliers and customers. It builds on the GHG Protocol standard in this area, adding the calculation and data-exchange consistency needed to trust the numbers shared between companies. PACT’s purpose with this standard is to replace estimated Scope 3 data with verified primary data from trading partners.
ISAE 3000 (Revised) and ISAE 3410
The assurance standards most sustainability engagements used before ISSA 5000’s effective date of December 15, 2026.
GHG Protocol (Corporate Standard and Scope 3 Standard)
The underlying accounting framework that every standard builds on top of.
ISO 14065 and ISO/IEC 17029
These standards are for the auditors, not your company. ISO/IEC 17029 is a general baseline standard and ISO 14065 adds GHG-specific competence, impartiality, and quality-management requirements on top of it. Find auditors who are accredited under ISO 14065 or through a national accreditation body.
How to use automation to prepare for a sustainability data audit
Sustainability audit standards can look like a patchwork of checklists. In practice, they all serve one purpose: making sure the data is sufficiently reliable, so the reader can act on the data. This can mean making an investment decision, changing an operating policy or getting outside financing.
Data that meets this bar is called decision-useful: reliable enough to support a real decision. That’s the same standard financial audits have applied for decades, and it’s now the gold standard for sustainability audits too.
Keep decision-usefulness in mind when you design your system. Every test and validation point should eliminate ambiguity and add confidence to the data. That takes precise specifications, tests that check tests, and ongoing monitoring.
Obviously automation plays a key role. There is simply too much to do, and it must be done methodically. Ad hoc systems lead to costly audits and the risk of qualifications. Here’s a checklist that demonstrates how automation ties a sustainability data system together, while making it fully audit-ready.
Audit-readiness checklist: 10 things auditors check
| What auditors check | Typical point of failure | How to automate | The associated audit standard |
|---|---|---|---|
| Scope and BoundariesWhich assets, facilities, and entities are in vs. out | Boundaries are often assumed rather than documented, and drift as the business changes. | Set boundaries with version controlled reference tables, and automatic testing for drift. | GHG Protocol boundary rules; ISSA 5000 scope requirements. |
| Duplicate DetectionWhich bills and invoices have been re-issued, corrected, or re-sent | Duplicate files and duplicate content must be detected. | Establish multiple layers of duplicate tests, using different rules and technologies. | ISO 14064-3 verification procedures (data reliability testing). |
| Field-Level DefinitionsHave a complete data dictionary | “Energy use” can mean three different things across three regional teams. | Enforce one schema: at ingestion, at extraction and with customized files. | GHG Protocol definitions; ISSA 5000’s “suitable criteria” requirement. |
| Consistency Over Time | Year-over-year swings get blamed on “the business changed” when it is the method that changed. | Version-controlled extraction and calculation logic, enabling like-for-like comparisons that tell the full story. | The consistency principle shared by ISO 14064-1 and the GHG Protocol. |
| Primary DataBe ready to report the share of your data that comes from actuals, not estimates | Estimates are mixed into totals with no record of which figures were real. | Automated data capture delivers an abundance of data, making primary data the default. | WBCSD’s PACT Methodology; CSRD and CBAM estimation caps. |
| Data LineageEvery step from source document to reported number | Incomplete and fragmented documentation means auditors struggle to recreate the steps. | System-generated lineage records: every transformation logged automatically. | ISSA 5000 evidence and documentation requirements. |
| Assumptions & RulesThere is a systematic and logical way to apply these when needed | Workarounds live in one person’s inbox or memory. | Built-in monthly or quarterly reviews require reconciliation of data issues before they are forgotten. | ISSA 5000 professional-judgment documentation requirements. |
| Gap RemediationMissing files or missing data actively chased down | Software or humans fill in data gaps with no flag, and nobody notices until assurance does. | Automated gap detection throughout the year gives sustainability teams time to take action. | ISO 14064-1 completeness principle. |
| Validation TestingMultiple layers of accuracy and data handling tests | One-time spot checks before the report ships leave many hidden errors. | A library of automated test scripts, specialized by data processing step, and using different tools and technologies. | ISO 14064-3 verification methodology. |
| Post-Integration ValidationA second check after data lands in the target system | Mapping errors between systems go undetected until an auditor finds them. | Automated post-load checks that re-validate data once it’s in the next system, e.g. reporting software, ERP and so on. | ISSA 5000 evidence-sufficiency requirements. |