Areebi vs Redactive: Secure AI Control Plane vs Permissions-Aware AI Data Layer (Australia)

Choose Redactive if your number-one concern is that AI will let people read data they are not authorised to see; choose Areebi if you need a single runtime control plane that governs everything that happens between your users and your LLMs. The two products solve adjacent problems at different layers of the AI stack, so the honest framing is not a winner-takes-all fight - it is a question of which risk you are buying down first.

Redactive publicly positions itself around permissions-aware data access for enterprise AI. It sits in front of your knowledge bases and acts as a pre-retrieval guardrail, so that when an AI assistant or RAG pipeline goes to fetch context, it only ever retrieves content the end user is actually entitled to. Redactive also analyses and remediates the misconfigured source-system permissions (what it calls "data access debt") that AI would otherwise expose at speed. This is a deep, well-defined problem, and Redactive is a recognised specialist in it - it names Australian customers including PEXA and HESTA and, since October 2025, sits inside RecordPoint's broader data-security and governance platform.

Areebi is positioned differently. It is a Secure AI Control Plane - a runtime layer that sits between your users and any LLM and is designed to enforce controls on live AI traffic: real-time data-loss prevention, a policy engine, immutable audit logging, output guardrails, shadow-AI discovery and access control. Permissions-aware access is one capability inside that broader runtime, not the whole product. Areebi is also privately deployable (Docker, Kubernetes, on-premises or private cloud) so that data and logs stay in Australia.

So the buyer's question is really: is my most acute gap at the data-retrieval layer (who can see what), or at the runtime-enforcement layer (what is allowed to flow to and from the model, and can I prove it)? If it is the former, Redactive's specialism and track record are hard to beat today. If it is the latter, Areebi is built for that job. And in many regulated environments, both layers are worth having.

The clearest way to compare these products is by where they operate, not by trading feature checklists.

Redactive operates at the data and retrieval layer. Its job is to make sure the data an AI system can reach is correctly permissioned. Per its own public materials, Redactive analyses unstructured content semantically - at the chunk, sentence and image level - identifies anomalous or misconfigured access, and can automatically remediate those permissions. At query time, it acts as a pre-retrieval guardrail in front of tools such as Microsoft Copilot, Glean or a custom RAG application, leveraging existing SSO, LDAP and DSPM signals rather than replacing them. Redactive also publicly offers a browser-level prompt-security capability that applies DLP rules to prompts and surfaces shadow-AI usage in tools such as personal ChatGPT accounts. In other words, much of Redactive's public positioning answers the question: "Should this user be allowed to retrieve this content at all, and is sensitive data being pasted into AI tools?"

Areebi operates at the runtime control layer. Its job is to govern the live conversation between a user and a model - regardless of which model or which retrieval system is behind it. Areebi is designed to inspect prompts and responses in real time for sensitive data (DLP), apply a configurable policy engine to decide what is allowed, write an immutable audit record of every interaction, enforce guardrails on model outputs, discover unsanctioned "shadow AI" usage, and apply access controls. Areebi answers a different question: "Given everything flowing to and from the model right now, what should be allowed, blocked, redacted or logged - and can I prove it later?"

These are complementary. A permissions-aware retrieval layer can ensure only authorised data reaches the model, while a runtime control plane can ensure that whatever is exchanged with the model is screened, governed and recorded. Neither fully substitutes for the other. A buyer who already runs Redactive may still want broad runtime DLP, output guardrails and immutable audit across every model; a buyer who already runs a control plane may still want deep permission remediation at the source. The mistake would be to assume one product's strength implies the other's weakness - they are simply scoped to different jobs.

An honest comparison has to start by conceding a competitor's real advantages, and Redactive has several that Areebi does not yet have.

• Depth in permissions-aware retrieval. Redactive's entire focus is the data-authorisation problem for AI. Per its public materials it works at a granular level - chunk, sentence and image - and can automatically remediate misconfigured permissions "in minutes, not months." This is a hard, specialised capability, and Redactive is a recognised specialist in it.
• Named Australian deployments and a referenced track record. Redactive publicly names flagship Australian customers including the property-exchange platform PEXA and the superannuation fund HESTA. For a regulated buyer, production references of this kind carry real weight.
• It is a shipping, acquired, established product. Redactive was acquired by RecordPoint in October 2025. RecordPoint is an established data-security and governance vendor whose own publicly named customers include major Australian banks (Westpac, NAB, Macquarie) and regulators (APRA, ASIC). That backing brings maturity, scale and a broader governance portfolio behind the technology.
• Source-permission remediation, not just enforcement. Beyond gating retrieval, Redactive publicly positions itself as finding and fixing the underlying permission misconfigurations across systems - useful work that improves an organisation's overall data-security posture, independent of any single AI tool.

Areebi should be measured honestly against this. Areebi is earlier-stage: it is in stealth, has no named customers to cite, and its SOC 2 readiness is in progress rather than certified. On commercial maturity, named-customer proof and the depth of source-permission remediation, Redactive is ahead today, and a credible buyer should weigh that.

Areebi's value is breadth of runtime control and the deployment model, rather than depth in any single retrieval problem. The following describe Areebi's intended architecture and scope as a control plane.

• Real-time DLP on live AI traffic. Areebi is designed to inspect prompts and model responses as they flow through the control plane, so sensitive data can be detected, redacted or blocked at the moment of the interaction - covering traffic to and from any connected model, not only retrieval from a connected knowledge base.
• A policy engine across models and use cases. Areebi centralises the rules for what is permitted - which users, which models, which data classes, which actions - so policy is enforced consistently regardless of the underlying LLM or application.
• Immutable audit logging. Areebi is designed to record every interaction in a tamper-evident log, so an organisation can reconstruct and evidence who asked what, what the model returned, and which controls fired - the kind of evidence regulated industries need for assurance and investigations.
• Output guardrails and shadow-AI discovery. Areebi applies guardrails to model outputs at runtime and discovers unsanctioned AI usage across the organisation, helping bring "shadow AI" into a governed perimeter.
• Private, sovereign deployment. Areebi can be deployed via Docker, Kubernetes, on-premises or in a private cloud, so prompts, responses, logs and policy stay within an organisation's Australian environment. For buyers with data-residency or sovereignty obligations, the deployment model itself is a core part of the value.

Note the scope difference: Redactive concentrates on doing the data-access layer - permissions-aware retrieval, with browser-level prompt security alongside it - very well, while Areebi aims to be the single enforcement and audit point for AI traffic across the runtime. Both are legitimate strategies, and the right one depends on the gap you are trying to close.

Both products are relevant to Australian regulated enterprises, and both can support the same obligations from different directions - so this is another area where they complement rather than compete.

Frameworks such as APRA CPS 234 (information security) and CPS 230 (operational risk management), the Privacy Act and the Australian Voluntary AI Safety Standard all push regulated entities toward demonstrable control over how sensitive data is accessed and used by AI. Redactive's contribution is at the access layer: by aiming to ensure AI only retrieves data a user is entitled to, and by remediating misconfigured permissions, it can reduce the risk of inappropriate disclosure - directly relevant to information-security and privacy obligations.

Areebi's contribution is at the runtime and evidence layer: real-time DLP is intended to reduce the chance of sensitive data leaving the organisation through a prompt or response, the policy engine makes control decisions explicit and consistent, and immutable audit logging is designed to produce the kind of records an auditor or regulator can rely on. For obligations that turn on being able to prove a control operated - not just assert it - a runtime control plane with tamper-evident logs is well aligned.

To be clear about Areebi's status: it does not hold certifications today. SOC 2 readiness is in progress and not yet certified, and Areebi has no analyst ratings or named-customer attestations to point to. Buyers who need certified third-party assurance now should weigh that, and should also recognise that RecordPoint (Redactive's acquirer) brings an established governance posture and regulator-facing customer base. You can read more about the specific obligations in our guides to APRA CPS 234 and AI, APRA CPS 230 and AI and the Australian Privacy Act.

Deployment. Areebi is designed to be privately deployed so that data never leaves the customer's environment - Docker, Kubernetes, on-premises or private cloud, with data resident in Australia. This matters most to organisations with sovereignty or residency constraints who want the enforcement point inside their own perimeter. Redactive is delivered as an enterprise platform that integrates with existing systems (SSO, LDAP, DSPM) and is also listed on AWS Marketplace; with the RecordPoint acquisition it sits within a broader governance platform. Buyers with strict in-perimeter requirements should validate each product's specific deployment options against their own controls rather than assume one model.

Maturity. This is where candour is most important. Redactive is a real, shipping product with named Australian customers and the resources of an acquirer behind it. Areebi is earlier-stage: in stealth, pre-named-customer, with SOC 2 readiness in progress (not certified) and no analyst recognition or awards to cite. If your selection criteria weight production references, third-party certifications and vendor scale heavily, Redactive is the more proven option today and we will not pretend otherwise. Areebi's differentiation is architectural - the breadth of runtime controls under one roof and the private, sovereign deployment model - not commercial maturity.

The practical takeaway for a buyer: shortlist on the basis of the specific risk you are closing. If it is permissions-aware retrieval, Redactive's specialism and track record lead. If it is unified runtime enforcement and auditability with in-country deployment, Areebi is purpose-built for that, and you should evaluate it on whether its architecture fits - while pressure-testing its maturity through a proof of concept. For many, the strongest posture combines a permissions-aware data layer with a runtime control plane.