Enterprise-Grade AI Memory: Security, Compliance, and Scale
Deploying AI memory at the enterprise level is not the same problem as adding memory to a developer prototype. Enterprises need audit trails, role-based access, data residency controls, compliance certifications, and integration with existing systems. MemoryLake is built to satisfy each of these requirements.
Enterprise-Grade AI Memory: Security, Compliance, and Scale
Get Started FreeFree forever · No credit card required
The Memory Problem
Most AI memory solutions are designed for developer convenience, not enterprise deployment. They lack formal compliance certifications, offer no access control model, provide no audit trail, and give IT and security teams nothing to evaluate against existing standards. Deploying them in a production enterprise environment requires custom security wrapping that defeats the purpose of using an off-the-shelf solution.
What MemoryLake Does Differently
Compliance certifications ready for vendor review
MemoryLake is ISO 27001 and SOC 2 Type II certified, and compliant with GDPR and CCPA. These are not self-assessed claims — they are audited certifications that satisfy standard enterprise security review requirements.
Git-like versioning with full memory provenance
Every memory write is versioned. Every version includes source attribution, timestamp, and a complete change history. Memory provenance is available for audit at any time, for any memory item, without additional tooling.
Role-based access control at the memory level
Access is not binary. Enterprise deployments require different roles to have different permissions across different memory types. MemoryLake's RBAC model defines read, write, and administrative permissions at the memory category level.
Free forever · No credit card required
How It Works
- Connect — MemoryLake integrates with your existing enterprise infrastructure via 20+ connectors: Google Workspace, Office 365, Lark, Dingtalk, Dropbox, MySQL, PostgreSQL, Delta Lake, Apache Iceberg, and more.
- Structure — Memory is organized into six typed categories with enterprise access policies applied per category. The D1 Engine (Vision-Language Model) handles complex document ingestion from PDF, Excel, and other enterprise formats.
- Reuse — AI systems across your organization retrieve memory through a centralized, controlled layer. Every retrieval is logged. Every modification is versioned. Compliance is built into the operation of the system, not bolted on afterward.
Before & After
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Compliance certification | Custom assessment required per deployment | ISO 27001, SOC 2 Type II, GDPR, CCPA certified |
| Data encryption | Varies by implementation | AES-256 encryption at rest and end-to-end in transit |
| Audit trail | Manual logging or absent | Full memory provenance with versioning and source attribution |
| Access control | Custom per-system implementation | Role-based access control built into the memory layer |
| Enterprise integration | Custom connectors required | 20+ pre-built integrations with enterprise systems |
| Complex document ingestion | Standard parsers, limited format support | D1 Engine (VLM) handles complex PDF, Excel, and mixed formats |
Built For
MemoryLake enterprise deployment is designed for enterprise architects, IT leads, and CIOs evaluating AI memory infrastructure for organization-wide deployment. It is appropriate for regulated industries — financial services, healthcare, legal, and government — where compliance and audit trail requirements are non-negotiable.
Try this use case on your own account
Corvane Mutual gives three AI assistants access to one memory: the underwriting rule book, the open claim files and the HR investigations file. The demo creates a sandbox org team, makes each assistant a virtual member with its own API key and read or write access to the projects the policy names, then has every assistant try everything with its own key. It changes a grant, removes a member whose key leaked, and reads the audit log.
What you will see
- One member per assistant:
member create(virtual, no login),member scope setlimits it to the policy's projects,access set-levelrecords read or write,api-key create --memberissues its key - 27 calls (3 assistants × 3 projects × read / search / write), each with the assistant's own key: 27 of 27 server answers match the policy. Refusals are
403 ACCESS_DENIEDnaming the missing permission, e.g. "Add Memory" on the rule book access showlists who reaches the HR file, at what level and why. A quickaccess grant+set-level readturns out to be effectively write (levels only add);member scope setwith the read actions makes it read- A leaked key:
member removeand the same call is refused within a second (401); what it wrote stays.audit-log listshows every access change and the key that made it. Plus a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/enterprise-rbac-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
Watch it run · real, unedited recordings
Related use cases
Frequently asked questions
What compliance certifications does MemoryLake hold?
What compliance certifications does MemoryLake hold?
MemoryLake is ISO 27001 certified, SOC 2 Type II certified, and compliant with GDPR and CCPA. These certifications cover data security management, operational security controls, and data privacy requirements respectively.
Where is enterprise memory data stored and can we control data residency?
Where is enterprise memory data stored and can we control data residency?
MemoryLake supports data residency configuration for enterprise deployments. Contact the MemoryLake enterprise team for region-specific deployment options and data residency agreements.
How does role-based access control work across different AI models?
How does role-based access control work across different AI models?
Access control is enforced at the MemoryLake API layer, independent of which AI model is making the request. Each API credential is associated with a role that defines memory type permissions. The model identity is separate from the credential identity.