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11/07/2026 2:01 pm
TL;DR: Point-in-time compliance, manual evidence collection, shadow AI, and static trust artefacts will no longer keep pace with real-time systems, AI adoption, and continuous assurance demands, according to Drata. The core shift is from periodic validation to machine-readable trust that GRC, security, and AI governance teams can verify continuously.
NHIMG editorial — based on content published by Drata: predictions for 2026 on trust, AI governance, and continuous assurance
By the numbers:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, 46% confirmed and 26% suspected.
Questions worth separating out
Q: What breaks when compliance is still point in time in dynamic environments?
A: Annual compliance breaks when the control environment changes faster than the review cycle.
Q: Why do shadow AI tools create governance risk before they create security incidents?
A: Shadow AI creates governance risk first because it can operate outside inventory, ownership, and approval controls.
Q: How should security teams govern AI-assisted compliance outputs?
A: Teams should treat AI-assisted outputs as draft evidence, not authoritative proof.
Practitioner guidance
- Move from periodic audits to continuous assurance Tie evidence collection to live control state for access, configuration, and workflow ownership so review cycles do not outlive the systems they describe.
- Inventory shadow AI before policy expansion Discover unsanctioned AI tools and agents, map what data and identities they can reach, and assign an accountable owner before allowing broader use.
- Treat AI-generated evidence as untrusted until verified Require provenance, input traceability, and reviewer sign-off for any evidence or risk output produced with AI assistance, especially in compliance workflows.
What's in the full article
Drata's full post covers the operational detail this analysis intentionally leaves for the source:
- How Drata's leadership maps continuous assurance to specific GRC operating-model changes and executive accountability.
- The article's six-prediction structure, including the trust-passport concept and the shift to GRC plus assurance.
- Detailed examples of how AI agents may handle vendor reviews, evidence collection, and anomaly detection inside compliance workflows.
- The authors' own framing of how CISOs, boards, and trust functions may evolve as trust becomes a measurable asset.
👉 Read Drata's 2026 predictions on trust, AI governance, and continuous assurance →
Machine-readable trust and continuous assurance: what changes for GRC teams?
Explore further
11/07/2026 2:11 pm
Machine-readable trust is becoming a control plane issue, not a reporting issue. The article correctly points to continuous, API-verified assurance as the new baseline, but the deeper shift is that trust artefacts are turning into operational control inputs. That means identity, access, and evidence must all be machine-consumable if boards and regulators are going to rely on them. Organisations that still treat trust as a document-management problem will fall behind the systems they are trying to govern.
A question worth separating out:
Q: Who is accountable when machine-readable trust signals are wrong?
A: Accountability remains with the organisation that consumes the signal, not the signal itself. If a trust passport, attestation, or automated evidence feed is inaccurate, the control owner must still verify that the underlying access, control, or assurance claim is current and legitimate before acting on it.
👉 Read our full editorial: Trust becomes machine-readable as GRC shifts to continuous assurance
