Trust becomes machine-readable as GRC shifts to continuous assurance

At a glance

What this is: This is Drata’s forecast for 2026, arguing that trust, compliance, and AI governance will move from static review cycles to continuous, machine-verifiable assurance.

Why it matters: It matters because IAM, GRC, and security teams will need evidence, access, and accountability models that work continuously across human, machine, and AI-driven processes.

By the numbers:

• 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, 46% confirmed and 26% suspected.

👉 Read Drata's 2026 predictions on trust, AI governance, and continuous assurance

Context

2026 trust management will be shaped less by annual attestations and more by whether evidence, controls, and access decisions can be validated continuously across live systems. That shift matters for identity programmes because machine identities, delegated access, and AI-assisted workflows all create governance gaps that spreadsheet-led compliance cannot reliably track.

Drata’s predictions reflect a broader convergence across GRC, compliance, and AI governance: static documents are losing value where systems change faster than audit cycles. For IAM and PAM teams, that means trust evidence has to be treated as operational telemetry, not a once-a-year export.

The article’s starting position is typical for the market, but the implications are now more concrete than many programmes assume.

Key questions

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. The most common failure is stale evidence: permissions, workflows, and supplier access may be valid at audit time but already out of date in production. Continuous assurance closes that gap by verifying state continuously rather than assuming yesterday’s snapshot still represents today.

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. Once an unsanctioned tool can access data or act on behalf of a user, organisations lose visibility into accountability, data flow, and decision authority. That is an identity and assurance failure before it becomes a breach.

Q: How should security teams govern AI-assisted compliance outputs?

A: Teams should treat AI-assisted outputs as draft evidence, not authoritative proof. Require source traceability, reviewer validation, and clear ownership for any questionnaire answer, risk score, or control mapping produced by AI. Without those checks, automation can scale errors as quickly as it scales productivity.

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.

Technical breakdown

Why point-in-time compliance breaks in dynamic environments

Point-in-time compliance assumes control status is stable long enough for a periodic review to represent reality. That assumption fails when cloud permissions, secrets, AI workflows, and supplier connections change continuously. In practice, the gap is not just audit timing, but evidence freshness: a control can look effective at review time and be broken the next day. Continuous assurance replaces static proof with ongoing validation, API-backed evidence, and control monitoring that reflects current state rather than historical snapshots.

Practical implication: replace annual evidence collection with continuous control telemetry tied to identity, access, and configuration state.

How agentic AI changes trust and evidence models

Agentic AI introduces systems that can act, query, score, and generate evidence with limited human intervention, which changes both the speed and provenance of governance outputs. A model can assist with questionnaires or risk mapping, but it can also fabricate confidence if its outputs are not verified. This creates an assurance problem: teams must validate not only the result, but the chain of reasoning, inputs, and authority behind the result. AI governance therefore becomes a control problem, not just a policy problem.

Practical implication: require provenance, human accountability, and validation gates for any AI-assisted compliance output.

Shadow AI as an identity and governance blind spot

Shadow AI is not only an application risk. It is also a trust and identity problem because unsanctioned AI tools can inherit data access, act on behalf of users, and bypass normal approval paths. When employees introduce these systems outside official governance, organisations lose visibility into what data they touch, what decisions they influence, and what identities they expose. That makes discovery, classification, and access control the foundational requirements, not after-the-fact controls.

Practical implication: discover unsanctioned AI systems, classify their access paths, and bring them under identity governance before scaling use.

Threat narrative

Attacker objective: The objective is to exploit governance blind spots so unverified AI-driven outputs or hidden access paths can influence trust, compliance, or decision-making.

1. Entry occurs when employees adopt unsanctioned AI tools or agents to accelerate work, bypassing approved governance paths and visibility controls.
2. Escalation follows when those tools gain access to questionnaires, vendor records, evidence repositories, or workflow systems without clear ownership or review.
3. Impact emerges when AI-generated outputs, evidence, or trust signals are accepted as authoritative despite weak provenance, enabling compliance drift or misinformed decisions.

NHI Mgmt Group analysis

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.

Shadow AI creates an identity governance gap before it creates a data problem. Unapproved AI tools can act with delegated access, reuse human permissions, and move information through workflows faster than reviews can detect. That makes discovery and authorisation the first line of defence, not model scoring or policy language. The governance lesson is straightforward: if the AI system is not on the inventory, the trust model is already broken.

Continuous assurance will expose weak lifecycle controls in both human and non-human identity programmes. The article’s prediction about real-time evidence only works if access provisioning, review, and offboarding are already mature. In practice, machine identities and AI-assisted workflows will surface stale credentials, orphaned service accounts, and undocumented approvals faster than annual audits ever did. Teams should expect assurance tooling to act as a pressure test on IAM and NHI lifecycle discipline.

GRC is becoming an identity-adjacent discipline whether practitioners planned for it or not. As trust becomes measurable and portable, the boundary between GRC, IAM, PAM, and AI governance narrows. Control owners will need to prove who or what had authority, when it was granted, and whether it was still valid at the moment of use. The practical conclusion is that governance models now depend on identity evidence quality as much as policy design.

Trust passports are only useful if the underlying assurance is real. Structured, machine-readable trust signals can improve vendor diligence and onboarding, but they can also create false confidence if attestation quality is weak. The market will need stronger validation of the evidence behind the signal, especially where AI systems consume those signals automatically. Practitioners should treat portable trust as a governance accelerator, not a substitute for verification.

What this signals

Trust operations are converging with identity operations. As assurance becomes continuous, the same teams that manage access, privilege, and lifecycle will be asked to prove evidence quality in real time. That makes identity telemetry, control ownership, and auditability part of the same operating model, not separate disciplines.

Verification trust gap: organisations will increasingly need to prove not just that controls exist, but that the evidence behind those controls is current, attributable, and machine-readable. That is where NIST Cybersecurity Framework 2.0 and identity-centric lifecycle discipline begin to overlap in practice.

The operational signal for practitioners is clear: if a workflow can be automated, it can also be governed automatically. Teams should expect assurance tooling to expose stale permissions, unsanctioned AI use, and weak evidence lineage faster than quarterly reviews ever could.

For practitioners

• 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.
• Align IAM, PAM, and GRC ownership around one evidence model Standardise how identity, privilege, and control evidence is recorded so the same record can support access reviews, audits, and assurance reporting.
• Validate trust signals before automating decisions Test whether external trust passports or machine-readable attestations reflect current reality before letting them drive onboarding, access, or procurement steps.

Key takeaways

• Point-in-time compliance is becoming inadequate because trust now changes faster than audit cycles can capture.
• AI-assisted workflows and shadow AI create an identity and assurance problem before they create a model risk problem.
• Continuous assurance will only work if IAM, PAM, and GRC share one current evidence model for access, control, and accountability.

Key terms

• Continuous Assurance: Continuous assurance is the practice of validating control effectiveness and evidence in real time rather than at fixed audit intervals. It combines telemetry, workflow ownership, and automated checks so governance reflects current system state instead of a historical snapshot.
• Shadow AI: Shadow AI refers to AI tools or agents used inside an organisation without formal approval, inventory, or governance. These systems may access data, perform actions, or influence decisions outside the controls that normally govern identity, privilege, and accountability.
• Machine-Readable Trust: Machine-readable trust is trust expressed as structured, verifiable signals that systems and people can evaluate automatically. It replaces static documents with live evidence, but only works when the underlying assertions are current, attributable, and validated.
• Trust Passport: A trust passport is a portable, cryptographically verifiable trust signal that packages evidence, attestations, or policy state for fast consumption by other systems. Its value depends on the quality of the underlying evidence and the freshness of the controls it represents.

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.

👉 Drata's full post expands the trust-passport concept, shadow AI risk, and the shift from GRC to GRC plus assurance.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps practitioners connect lifecycle control, assurance, and accountability across identity programmes.