What is a Secure AI Control Plane? A Definitive 2026 Guide

Categories are not invented. They form when buyers stop asking which point tool they need and start asking what kind of system they need. The Secure AI Control Plane is a category because three independent forces have converged on the same architectural answer between Q4 2024 and Q2 2026: consolidation of AI-security vendors, regulatory codification, and the failure of the fragmented toolchain to contain shadow AI.

Force 1: Consolidation of the AI-security vendor stack

Through 2024 and 2025, every major security platform acquired one or more standalone AI-security startups and rolled them into a platform bundle. Cisco acquired Robust Intelligence in August 2024 to fold AI red-teaming and runtime model protection into the Security Cloud. Palo Alto Networks announced its agreement to acquire Protect AI in April 2025, rolling AI-SPM and model-supply-chain scanning into Prisma Cloud. CrowdStrike acquired Adaptive Shield (October 2024) for SaaS security posture that has increasingly absorbed AI-app oversight. Wiz acquired Gem Security (April 2024) for cloud detection and response with AI workload coverage. F5 acquired Heyhack (2024) to fold AI-application security testing into BIG-IP. The pattern is consistent: seven or more standalone AI-security companies were rolled into platform bundles in eighteen months. Independent point tools are no longer the unit of purchase; integrated platforms are.

From the CISO perspective, this is a forced choice. Either they buy a consolidated platform from one of the bundlers (with the lock-in and feature-parity trade-offs those entail), or they assemble a Secure AI Control Plane as a category alongside their existing security stack - governed by them, integrated to identity, and explicitly not bundled into a network-security platform whose incentives drift with each acquisition cycle.

Force 2: Regulation has caught up

The compliance landscape that existed in 2023 (we should probably have a policy) has been replaced by a 2026 landscape of enforceable, dated obligations. The EU AI Act (Regulation 2024/1689) entered into force on 1 August 2024. The prohibited-practices and AI-literacy obligations applied from 2 February 2025; obligations for general-purpose AI model providers and the governance framework applied from 2 August 2025; high-risk system obligations apply broadly from 2 August 2026, with extended transitions for embedded-product cases. Fines reach up to 7% of global annual turnover. The Act is not aspirational text; it is operative law, and Article 9 (risk management), Article 10 (data and data governance), Article 12 (logging), and Article 14 (human oversight) all require capabilities a Secure AI Control Plane delivers as a system.

Around the EU Act, the rest of the regulatory mosaic has accelerated. The NIST AI Risk Management Framework (AI 100-1, January 2023) became the de-facto US baseline for federal-adjacent enterprises, and the Generative AI Profile (NIST AI 600-1) published in July 2024 added GenAI-specific guidance. ISO/IEC 42001:2023 became the first certifiable AI Management System standard. The US Office of Management and Budget issued OMB Memo M-24-10 on AI governance for federal agencies in March 2024, and Singapore AI Verify Foundation extended the Model AI Governance Framework to generative AI. State-level US legislation has compounded the pressure: industry trackers indicate that more than 50 US states introduced AI-related bills through 2024-2025, with Colorado SB 24-205 the first comprehensive AI law to pass. Australia APRA has signalled an evolution of CPG 234 to address AI-specific operational risk. The cumulative effect is that an enterprise with multi-jurisdictional exposure can no longer satisfy its compliance obligations with a patchwork of point tools and a quarterly review.

Force 3: Shadow AI is unmanaged at scale

The third force is the failure mode the first two are responding to. Industry surveys through 2024-2025 indicate that a majority of mid-market regulated organisations have employees routinely using unsanctioned consumer AI tools (ChatGPT consumer, Claude.ai personal, Gemini, Copilot under personal accounts) to handle work tasks. Reporting from IAPP AI Governance Center and large vendor surveys converge on shadow-AI prevalence in the 60-80% range, with the highest concentrations in knowledge-work-heavy verticals: legal, financial services, healthcare, professional services. The exact number varies by survey methodology; the order of magnitude does not. The implication is that the existing security stack (CASB, SASE, DLP) does not see the majority of AI interactions an enterprise generates, and cannot govern them.

These three forces compound. Consolidation removes the just-buy-a-point-tool option. Regulation removes the we-will-write-a-policy option. Shadow AI removes the we-do-not-have-an-AI-problem option. The remaining choice is an architecture: a Secure AI Control Plane, deployed privately, governed by the enterprise, with the four capabilities below as non-negotiable controls.

The conceptual core of the category is the consolidation of four capabilities - Workspace, DLP, Policy, and Audit - under one operating model. Each capability is necessary; no three of them are sufficient. The capabilities are listed in deployment order, because that is the order in which a regulated enterprise typically encounters them.

Capability 1: Workspace

The Workspace is the user-facing surface where employees actually interact with AI. It is the part of the Control Plane your CEO sees in their browser and your customer-success team uses to draft email. If users do not prefer the Workspace to ChatGPT consumer, every other capability becomes irrelevant - traffic simply bypasses the system. The non-negotiable requirements for an enterprise Workspace are:

• Private deployment by default. The Workspace must run on customer infrastructure - private cloud, VPC, on-premises, or air-gapped - with no prompt or response data leaving the perimeter except through explicitly governed egress to model providers under a contractual data-processing agreement. SaaS-only delivery models are insufficient for the regulated mid-market.
• Model agnosticism. The Workspace cannot lock an enterprise into a single model provider. It must support OpenAI, Anthropic, Google Vertex, Amazon Bedrock, Azure OpenAI, Mistral, Meta Llama, Cohere, and any OpenAI-API-compatible endpoint (including on-prem deployments of open-weight models). The Control Plane abstracts model choice from the user and centralises governance regardless of where the request is fulfilled.
• Multi-modal. Text, structured retrieval, file upload (PDF, docx, xlsx, code), image generation and understanding, voice, and increasingly agentic flows. Each modality is a new ingress for sensitive data; each must be governable.
• RAG-native. Retrieval-Augmented Generation must be a first-class feature: secured connectors to corpus stores (SharePoint, Confluence, Drive, internal wikis, customer CRMs), encryption at rest, source attribution in responses, and access controls that respect the originating system permissions.
• Workspace isolation. Teams (HR, legal, engineering, customer support) must be able to operate in isolated workspaces with their own models, prompts, knowledge bases, and policies - while the Control Plane consolidates governance across all of them.

The Workspace is what makes the Secure AI Control Plane humane. Most enterprise security categories are defined by what they prevent. This one is defined first by what it offers users - and only then by what it controls.

Capability 2: DLP (AI-aware data loss prevention)

Traditional DLP - the kind embedded in Symantec, Forcepoint, Microsoft Purview, or Netskope - was designed for files, email, and tagged structured content. It assumes batch inspection of data at rest or in motion across well-defined boundaries (mailbox, file share, web upload). AI DLP is a different problem. It inspects free-form conversational input, streaming token-by-token responses, multi-turn context that accumulates sensitivity across turns, and structured outputs (JSON, code, SQL) where one redacted token can change meaning. The non-negotiables are:

• Real-time, streaming inspection. AI responses stream tokens at hundreds-of-tokens-per-second. DLP cannot wait for the full response to complete. It must inspect on a sliding window, redact mid-stream, and tear down the stream if a policy violation crosses the line - all under tens of milliseconds of added latency.
• Prompt and response symmetry. A response that regurgitates training data, leaks PII from a RAG corpus, or reveals other user data is as much a DLP event as a prompt that exfiltrates customer SSNs. The Control Plane must inspect both directions equally.
• Context-sensitivity. A statement like John Smith called about his account is sensitive in a prompt to a public model; explain how account verification works is not. AI DLP must go beyond pattern matching to use classifiers that understand semantic context - and must do so without sending the content back to a third-party model for inspection.
• Policy-aware actions. Block, redact, mask, tokenise, escalate, or log - each AI DLP event has multiple possible dispositions. The action is determined by the workspace, the data type, the user role, the model being used, and the export destination. This is where DLP intersects with Policy.
• PII/PHI/PCI/secrets coverage. The full taxonomy: names, emails, phone numbers, SSNs, passport numbers, financial account numbers, credit card numbers, patient identifiers, ICD codes, source-code secrets, API keys, cloud credentials, JWT tokens. Each enterprise will extend the taxonomy with its own classifications (deal codes, customer IDs, project codenames).

Deep coverage of AI-aware DLP is discussed in the dedicated What is AI DLP? definition. Within the Control Plane, DLP is the capability that prevents the runtime data leakage that auditors and regulators examine first.

Capability 3: Policy (machine-readable, version-controlled)

Policy in a Secure AI Control Plane is not a Word document. It is executable, versioned, reviewable code (or visual rules with a code representation underneath) that the Control Plane evaluates on every interaction. The four properties that separate Control Plane policy from a written acceptable-use policy are:

• Machine-readable. Policy is expressed in a schema the runtime can evaluate deterministically. A typical rule binds a workspace, role, data classification, model, and disposition into a single statement. There is no ambiguity at runtime because the rule has a single canonical evaluation.
• Version-controlled. Every change to policy is a versioned event with author, timestamp, justification, and approval. Auditors can ask what was the policy on 14 March 2026 and the answer is a hash, not an estimate.
• Testable. The Control Plane must support a policy simulator: feed historical interactions or synthetic scenarios through a proposed policy and report the disposition delta before promoting the change to production. Without simulation, policy changes are released untested - a practice no other security control accepts.
• Template-aligned. Out-of-the-box templates map to NIST AI RMF, ISO/IEC 42001, EU AI Act, HIPAA, and SOC 2 so customers do not start from a blank file. Templates are a forcing function: they encode the policy primitives the category requires, and a vendor that cannot ship them is signalling capability gaps.

See the AI Policy Engine definition and Areebi platform overview for how Policy interacts with the other capabilities.

Capability 4: Audit (immutable, per-interaction, exportable)

The Audit capability is what a regulator examines when they ask to see the controls in operation. It is the only capability whose value is realised entirely after the fact - and yet it is the capability that decides whether the rest of the Control Plane is admissible as evidence. The non-negotiables are:

• Per-interaction record. Every prompt, every response, every tool call, every retrieval, every DLP event, every policy evaluation, and every user identity is recorded as a structured event tied to a single interaction ID. There is no sampling.
• Immutable. Audit storage uses WORM (write-once-read-many) primitives - object lock on S3, ledger tables, or append-only databases - with cryptographic chain integrity (each record links to the prior via a hash). A compromised administrator account cannot retroactively rewrite the record without producing a detectable break.
• Tamper-evident export. Audit records must export to common GRC platforms (Drata, Vanta, OneTrust, ServiceNow GRC, Hyperproof, AuditBoard) and to raw formats (SIEM, S3, Snowflake) with the cryptographic chain preserved so external systems can independently verify integrity.
• Retention with deletion governance. Retention policies must be configurable by jurisdiction (the EU AI Act expects technical-documentation retention for at least ten years for high-risk systems) while still respecting GDPR erasure rights - a tension only solvable with per-record disposition rules and cryptographic shredding.
• Regulator-readable. Audit must surface to a non-engineer reviewer through a UI that filters by user, workspace, model, classification, event type, and time range - not just an API. A regulator will not query a database. They will scroll a screen.

The four capabilities are listed below as a one-page reference. In practice, a vendor that ships three of the four is in the market, but not a Secure AI Control Plane. The shorthand to evaluate any vendor is to ask: show me your Workspace, show me a streaming DLP intercept, show me your policy diff for last quarter, and show me a tamper-evident audit export. The four answers, together, are the category.

Capability matrix (one-page reference)

Capability	Non-negotiable controls	Failure mode if absent
Workspace	Private deploy, model agnostic, multi-modal, RAG-native, workspace isolation	Users default to consumer AI; shadow AI explodes
DLP	Streaming inspection, prompt+response symmetry, semantic context, configurable actions	PII/PHI leaves the perimeter; breach disclosure becomes routine
Policy	Machine-readable, versioned, testable, template-aligned to NIST/ISO/EU AI Act	Policy is theatre; enforcement is undocumented and inconsistent
Audit	Per-interaction, immutable, tamper-evident export, retention with deletion governance	No regulator-admissible evidence; controls are unprovable

Category-defining work is half what it includes and half what it excludes. The Secure AI Control Plane sits adjacent to several other categories that buyers will conflate it with, and naming the difference clearly is what allows architecture decisions to be made on first principles rather than by vendor pitch.

Not an AI Gateway

An AI Gateway routes requests between users and models, manages keys and quotas, handles failover, and reports token spend. It is a traffic layer. It does not include a workspace, does not perform AI-aware DLP, and typically does not generate regulator-admissible audit. A Control Plane can use a Gateway as its routing component, but a Gateway alone is not a Control Plane.

Not an AI Firewall

An AI Firewall is a prompt and response filter. It blocks known attack signatures (jailbreaks, prompt-injection patterns, malicious tool calls). It is a feature inside DLP, not a category. A firewall does not provide workspace, does not version policy, and does not produce audit at the depth a regulator requires. The Control Plane subsumes the firewall.

Not a DLP-only tool

An AI-aware DLP product (whether standalone or embedded in a broader DLP suite) covers the data-protection capability and nothing else. Without a workspace, employees still default to shadow AI. Without policy and audit, DLP events lack disposition logic and admissible evidence. DLP is one of four required capabilities; it is not the category.

Not an open-source AI workspace alone

LibreChat, AnythingLLM, Open WebUI, Lobe Chat, and similar open-source projects deliver a credible workspace and model abstraction. They are not, on their own, a Secure AI Control Plane. None ship enterprise-grade streaming DLP, immutable audit with cryptographic chain integrity, versioned policy as code with simulation, regulator-ready reporting, or commercial support for regulated environments. They are a reasonable starting point for a single team or a proof-of-concept; they are not a sufficient enterprise platform.

Not AI-CSPM (cloud security posture for AI)

AI-Security Posture Management products scan cloud configurations for AI-related misconfigurations - exposed model endpoints, unencrypted training data buckets, IAM gaps around AI services. They are valuable, but they operate on configuration at rest. The Control Plane operates on interactions at runtime. The two are complementary, not substitutable.

Not a general-purpose compliance management platform

GRC platforms (Drata, Vanta, OneTrust, ServiceNow GRC) collect evidence, manage controls catalogs, and orchestrate audits. They do not see AI interactions in real time, do not block policy violations as they happen, and do not produce the raw audit stream the Control Plane generates. They are downstream consumers of Control Plane output, not substitutes for it.

The Control Plane category lends itself to a six-level maturity model, anchored on the same conceptual scale as the NIST AI RMF (AI 100-1) maturity tiers (Initial, Managed, Defined, Quantitatively Managed, Optimized) and adapted to the four-capability definition. Each level has a concrete observable signal - the thing an external assessor sees first when evaluating posture.

Level	Name	Observable signal
0	Unmanaged	No sanctioned AI tool; employees use consumer AI on personal accounts; no policy
1	Acceptable use	Acceptable-use policy issued; no technical enforcement; no audit
2	Sanctioned tool	Enterprise account with one model provider; basic SSO; no DLP, no audit at depth
3	Controlled	Private workspace deployed; streaming DLP on; policy in a Word doc; some audit logging
4	Governed	All four capabilities operational; policy in code; immutable audit; quarterly attestation
5	Optimized	Continuous evidence to GRC; cross-jurisdiction policy diff; automated regulator-ready reporting

Level 0: Unmanaged

The starting point for most mid-market regulated organisations as of mid-2026. There is no sanctioned AI tool; employees use ChatGPT consumer, Claude.ai, Gemini, or Copilot under personal accounts. There is no policy beyond an informal be-careful. Shadow AI prevalence is uncontested because there is no path to govern it. Most boards have asked the question; most executive teams have not yet acted.

Level 1: Acceptable use

The CISO has issued an acceptable-use policy. It exists as a Word document attached to the security policy library. There is no technical enforcement; the policy is a signal of intent and a defense for the next breach disclosure. Audit consists of everyone-agrees-they-read-it. This is a defensible holding pattern for a quarter; not for a year.

Level 2: Sanctioned tool

The organisation has procured an enterprise account with one model provider (most commonly ChatGPT Enterprise, Anthropic Teams, or Microsoft Copilot for M365). SSO is wired up. Some configuration exists. There is no DLP at the runtime layer, no versioned policy, and no audit at a depth that satisfies the EU AI Act or NIST AI 600-1. Coverage is partial; employees in other roles continue to use other consumer tools.

Level 3: Controlled

A private workspace is deployed. Streaming DLP is operational and tuned to the organisation PII/PHI taxonomy. Policy is written but lives in a Word doc, with manual updates to the runtime. Audit logs exist but have not been tested for immutability or regulator export. This is where most early adopters land in 2026, and it is where the Control Plane category begins.

Level 4: Governed

All four capabilities are operational. Policy lives in version control with simulation. Audit is immutable, tested under tamper-evident export, and feeding the GRC platform. The organisation can pass a regulator review on AI without remedial effort. This is the destination state for a regulated mid-market enterprise through 2026.

Level 5: Optimized

The Control Plane continuously emits evidence to the GRC platform, and the organisation publishes a quarterly cross-jurisdictional policy diff. Regulator-ready reporting is automated. AI risk metrics flow into the board pack alongside other operational risk indicators. The Control Plane has become part of the organisation posture, not a project.

You can score your own posture against this scale using Areebi free NIST AI RMF gap analyzer or take the broader AI Risk Assessment.

No framework names the Secure AI Control Plane by name. Every relevant framework, however, requires capabilities that only a consolidated Control Plane can satisfy. The table below maps the four capabilities to the control families a regulated enterprise must answer to. The mapping is not exhaustive; it is the canonical 80% that an internal audit team will be asked about first.

Framework	Workspace	DLP	Policy	Audit
EU AI Act	Art. 14 (human oversight)	Art. 10 (data and data governance)	Art. 9 (risk management)	Art. 12 (logging), Art. 13 (transparency)
NIST AI RMF	MAP (context, scope)	MEASURE (risks, impacts)	GOVERN (policies, accountabilities)	MANAGE (response, monitoring)
ISO/IEC 42001:2023	Cl. 7 (support, resources)	Annex A.7 (data for AI systems)	Cl. 6 (planning), A.4 (policy)	Cl. 9 (performance evaluation)
HIPAA Security Rule	164.312(a) (access control)	164.312(e) (transmission security)	164.308 (administrative safeguards)	164.312(b) (audit controls)
SOC 2 (Trust Services)	CC6 (logical access)	CC6.7 (data classification)	CC8 (change management)	CC7 (system operations)
SG Model AI Governance	Internal governance structure	Operations management (data)	Decision-making model	Stakeholder communication

EU AI Act in particular deserves the most attention through 2026-2027. Articles 9-15 codify the obligations for high-risk systems: a documented risk-management system (Art. 9), data and data-governance practices for training, validation, and operation (Art. 10), technical documentation (Art. 11), automatic event logging (Art. 12), transparency and information to deployers (Art. 13), human oversight controls (Art. 14), and accuracy, robustness, and cybersecurity standards (Art. 15). Each maps to one or more capabilities of the Control Plane. The Act becomes broadly applicable from 2 August 2026; the time to evidence each capability is now.

For organisations operating in the United States, the NIST GenAI Profile (AI 600-1) is the practical operating reference. It overlays GenAI-specific risks onto the four core RMF functions, and a Control Plane is the operational answer to most of them. See Areebi dedicated guides for EU AI Act, NIST AI RMF, ISO/IEC 42001, HIPAA, and SOC 2 for capability-to-control crosswalks.

The market for Secure AI Control Plane capabilities is not yet consolidated under a single analyst category - the labels in market shift quarterly. The most useful way to map the landscape is by entry vector: where each category of vendor started, and what they have not yet built.

• Workspace-led (open source heritage). Vendors that began as open-source AI workspaces and have added enterprise governance over time. They ship credible Workspace and basic Policy, but typically lag on streaming DLP, immutable audit at depth, and regulator-ready evidence. Best fit: technical teams with strong internal security engineering willing to build the missing capability.
• DLP-led (data-protection heritage). Vendors that started in traditional DLP or cloud DLP and have extended into AI-aware inspection. Strong DLP and policy; weaker on Workspace, often delivered as a browser plugin or proxy. Best fit: enterprises whose primary concern is data leakage and who can afford the user-experience tax of a bolt-on workspace.
• Governance-led (GRC heritage). Vendors that started in compliance management and have added AI-specific controls. Strong Policy and Audit at the catalog level; weaker on runtime Workspace and streaming DLP. Best fit: organisations whose first audit is imminent and who need evidence quickly, even if runtime control is partial.
• Gateway-led (developer-platform heritage). Vendors that started as AI Gateways for developer platforms and have added governance. Strong routing and developer ergonomics; weaker on end-user Workspace, semantic DLP, and regulator-grade audit. Best fit: organisations whose AI use is primarily API-driven from internal applications.
• Full Control Plane. A small group of vendors that explicitly position around all four capabilities from the start, with private deployment and the regulated mid-market as their target buyer.

At Areebi, we built the Control Plane category as a deliberate architectural choice: every prompt and response in our customer AI estate is mediated by a single layer that consolidates Workspace, DLP, Policy, and Audit. We did this because the alternatives - a stack of point tools or an in-house build - leave the four capabilities partially implemented, with the gaps showing up in regulator review and breach disclosures rather than in product demos.

Analyst coverage is following the architectural pattern, though the labels in market reports still vary. We expect the next twelve months to see the major analyst houses publish bracket reports under names like AI Security Platforms, AI Trust and Safety Platforms, or GenAI Security and Governance. Whatever the label, the four-capability test is the durable one. See Areebi comparison guides for capability-by-capability evaluation against named alternatives, the platform overview for our implementation of the four capabilities, and the AI Control Plane landing page for the broader architectural narrative.

The following is a 90-day plan for a mid-market regulated organisation moving from Level 0 or 1 to Level 4 of the maturity model. It assumes a commercial Control Plane (not an in-house build) and a private deployment in the customer cloud or on-premises. Adjust each phase for organisations larger than 1,000 seats or with high-risk AI use cases under the EU AI Act.

Week 1-4: Foundation

Goal: a private Workspace in production with SSO, identity, and the first sanctioned models.

• Week 1. Stakeholder kickoff: CISO, compliance officer, GC, head of AI/data, line-of-business sponsors. Capture the top three use cases, the top three data classifications in scope, and the top three forbidden outputs. Take Areebi AI Risk Assessment to baseline. Score posture with the NIST AI RMF gap analyzer.
• Week 2. Deploy the Control Plane to the target environment (private cloud, VPC, or on-premises). Wire SSO via SAML/OIDC to the corporate IdP (Okta, Entra, Google). Configure the first two model providers (commonly Azure OpenAI plus Anthropic via AWS Bedrock).
• Week 3. Onboard the first 50 users from a single business unit (typically marketing, customer success, or product). Capture baseline usage data: which models, which prompts, which file types, which workspaces. This is the evidence base for the DLP tuning in weeks 5-8.
• Week 4. Publish a v1 acceptable-use policy mapped to the Control Plane policy primitives. Stand up the audit pipeline and verify immutability with a tabletop exercise. Confirm the GRC export path (Drata, Vanta, or the in-house equivalent) is operational.

Week 5-8: Policy layer

Goal: machine-readable, version-controlled policy operating on every interaction, with streaming DLP tuned to the enterprise data taxonomy.

• Week 5. Build the policy code from the v1 acceptable-use document. Use Control Plane templates aligned to EU AI Act, NIST AI RMF, and the organisation applicable industry framework. Set up the policy simulator and run last month traffic through the proposed policy. Record the disposition delta and have the CISO and GC sign off.
• Week 6. Tune AI DLP. Most enterprises ship with vendor-default classifiers (PII, PHI, PCI, secrets) and add custom classifiers for their own taxonomy (deal codes, project codenames, customer identifiers). Run the classifiers against the baseline traffic and tune for false positives. Capture the false-positive rate as a baseline metric.
• Week 7. Roll out the Control Plane to the next business units in waves of 100-200 users. The wave cadence is set by the helpdesk capacity to onboard. Activate workspace isolation for legal, HR, and any other groups whose data classification requires it. Begin shadow-AI displacement: communicate the sanctioned path and start blocking known consumer-AI endpoints at the egress layer.
• Week 8. First quarter-end posture review. The CISO and compliance officer assess the Control Plane against the four-capability checklist and identify any gaps. Update policy in code; promote to production via the versioned workflow.

Week 9-12: Audit and compliance

Goal: regulator-ready audit and continuous compliance evidence into the GRC platform.

• Week 9. Generate the first compliance evidence package for the organisation primary framework (typically SOC 2 for North American enterprises, ISO/IEC 42001 or EU AI Act for EU-exposed enterprises, HIPAA for healthcare). Use Control Plane evidence templates aligned to the relevant framework. Run a tamper-evident export drill and verify that integrity checks pass downstream.
• Week 10. Tabletop a regulator review. Pick one user, one workspace, one model, and one date range. Reconstruct the interactions from the audit log. Walk a compliance officer through what they would show a regulator. Fix every gap you find.
• Week 11. Operationalise continuous evidence. Configure the Control Plane to emit framework-aligned events to the GRC platform on a cadence (daily for SOC 2, weekly for ISO/IEC 42001). Set up the executive dashboard for AI risk indicators.
• Week 12. Final posture review. Score against the maturity model. Plan the next quarter: expansion to remaining business units, additional models, additional jurisdictions, and the move from Level 4 to Level 5.

For organisations that want a detailed framework reference while running this plan, the Areebi blog publishes quarterly deep-dives on each phase. The platform overview documents the Areebi Control Plane implementation of each capability above.