Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Drata: “2026 Trends in Trust: Drata Leaders Predict How Security, AI, and Assurance Will Converge”.
Key questions
Q: How do teams keep compliance evidence defensible across changing systems?
A: They should define proof requirements, capture evidence close to the source, preserve the original artifact, and keep the validation history attached to the governed item.
Q: Why do AI agents create new governance risk for GRC programs?
A: AI agents can request access, process data, and generate trust artefacts faster than human review cycles can keep up.
Q: What are the signs that shadow AI is weakening trust controls?
A: The main signs are unapproved tools appearing in workflows, evidence being produced outside approved systems, inconsistent approvals, and unclear ownership when decisions go wrong.
Practitioner guidance
- Map compliance evidence to live control sources Replace spreadsheet-based evidence gathering with integrations that pull from identity, cloud, ticketing, and security systems so control status is continuously verifiable.
- Classify AI agents as governed non-human identities Assign ownership, privilege boundaries, and review cycles to AI agents that handle access provisioning, risk scoring, or evidence collection.
- Define assurance checkpoints for shadow AI Create discovery, approval, and monitoring controls for unsanctioned AI tools and agent workflows before they create unreviewed data exposure or compliance drift.
Bottom line: Manual compliance evidence collection is too slow for AI-driven and continuously changing environments, so assurance has to become machine-readable.
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11/07/2026 2:11 pm
Machine-readable trust is becoming the new control plane for governance. Static artefacts such as PDFs, spreadsheets, and point-in-time attestations are too slow for environments where evidence, access, and controls change continuously. The operational shift is not cosmetic. It moves assurance closer to how modern systems actually behave, which means practitioners must treat trust data as a live dependency rather than a reporting output.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations prepare for machine-readable trust in vendor assurance?
A: They should standardise control outputs, define authoritative evidence sources, and ensure trust data can be verified automatically by both humans and systems. The practical test is whether a partner can validate posture without waiting for manual documents. If not, the assurance model is still too brittle for continuous business use.
👉 Read our full editorial: Trust becomes machine-readable as GRC shifts to continuous assurance