Data Lakehouse Security: Supporting Scalable Analytics and AI Workloads

In a lakehouse, object storage is the foundation. It holds all raw and modeled data and exposes it through the S3 API to analytics engines, AI frameworks, and governance tools. To support these use cases, object storage must do more than store data. It must provide fine-grained access control, encryption for data at rest and in transit, immutability with object locking, integrated key management, audit logging, and multi-site replication, all while delivering high performance and elastic scalability.

Above the storage layer, open table formats such as Apache Iceberg add structured metadata, schema and partition evolution, and time-travel. Catalogs like Polaris and Nessie manage this metadata and enforce governance rules at the table and branch level. Together, these components form the backbone of a secure and compliant data lakehouse.

This guide outlines the features to look for in an object store system, how to secure the data and metadata layers, and how to meet governance and compliance requirements without sacrificing performance or flexibility. The only useful data lakehouse solution is a secure one.

Securing Data in On-Prem and Air-Gapped Networks

Many industries demand that sensitive data stay within tightly-controlled infrastructure (finance, government, healthcare, etc.). Running your data lakehouse on-prem often in air-gapped environments provides full control over the network and hardware. In an air-gapped deployment, nodes cannot reach the Internet (often accessible only via a bastion host or VPN). This isolation is deliberate: “you cannot afford to expose” your data to the outside world.

If an airgapped environment isn’t possible, a way to secure your stack is by deploying inside segmented VLANs or private zones. In this way, you ensure that only trusted applications and users (behind the firewall) can talk to the storage, eliminating the risk of external attack. All software dependencies (OS packages, binaries, etc.) are staged on an internal “ops” network so updates come from verified internal mirrors. This approach gives data engineering teams complete control over their infrastructure stack.

Ensuring Availability and Durability at Scale

High availability is non-negotiable in a production lakehouse. Modern object storage delivers durability and resiliency via multiple layers. These features are non-negotiable for data lakehouse security.

Erasure coding & self-healing

Every object is automatically sharded and encoded (e.g. using 8+4 or higher erasure code by default) across all nodes. This inline erasure coding requires far less storage overhead than traditional triple-replication (HDFS) while tolerating multiple disk/node failures. If even a single shard becomes corrupted (bit-rot), Bitrot detection will heal it on the fly using parity shards. Unlike legacy storage, there can be no silent data decay.

Active-active, multi-site replication

For reliability on-prem, data lakehouses should employ true multi-site, active-active bucket replication. With this approach, you configure identical buckets across two (or more) datacenters so that writes to any site are replicated in near real-time to the others. It’s important that the buckets have the same name at each site, and that replication of object versions, tags, and S3 Object Lock retention info occurs atomically. In practice, this means any application (or person) can fail over to the remote site without changing bucket names or paths, as the objects and their metadata remain identical. Replication should use a combination of synchronous and eventual modes: within a site, it behaves with strict consistency, and across WAN, it should propagate changes as quickly as bandwidth allows. Notifications should be configured so that admins are alerted to any replication lag or failure. By leveraging dark fiber or private links between datacenters, this geo-replication can occur entirely off the public internet. The result should be near-zero RPO/RTO for mission-critical data. Even if an entire site fails, all data should remain available and in sync at the other site.

Linear scalability

Scaling is critical. The data lakehouse storage layer must be able to scale from 8 nodes up to hundreds. Each cluster must operate as a single logical namespace (single S3 endpoint) even as you add nodes. There should be no siloed “hot” master server; every node should participate equally. This design allows for simple growth: You should just be able to add more nodes to scale out. Swap and drop on standard hardware should be an absolute given when it is time to upgrade or modernize. The only thing that scales is simplicity.

Fine-Grained Access Control and Compliance

A modern lakehouse must satisfy strict governance and regulatory requirements (GDPR, HIPAA, etc.). While some of these requirements can and should be met further up in the stack in the compute layer, the storage layer for the data lakehouse should also enforce data governance. For example, in order to be a first class citizen for security in the data lakehouse stack, the underlying object storage must have the following features:

Identity and Access Management

The underlying storage must have IAM that lets you define users/groups and attach fine-grained policies at bucket/object level. In practice, this could mean you could grant a data science team read-only access to a specific bucket containing curated datasets, while giving an ingestion pipeline full write access to a staging bucket. You should be able to integrate with existing directory services (LDAP/Kerberos/OAuth) for SSO, or manage credentials directly. Multi-tenancy should be supported via “encryption enclaves,” allowing different departments to have isolated keys and access domains. This ensures one team cannot decrypt another’s secure data.

Encryption and Key Management

As mentioned, all data should be encrypted in transit (TLS) and at rest. The industry-standard AES-256-GCM, ChaCha20-Poly1305, etc., with full SSE (server-side encryption) compatibility. Keys should be managed internally or via an external HSM/KMS. This covers regulations like HIPAA (§164.312(a)(2)) and GDPR (pseudonymization), because sensitive information can be encrypted with keys you control. A KMS that is native to the underlying storage layer is an attractive option, especially for greenfield deployments.

Object Lock & Retention

For compliance, the underlying object storage should allow for the application a WORM retention policy so that objects cannot be deleted or altered until a given date. “Legal hold” flags can prevent deletions indefinitely if needed. These features help meet SEC, FINRA, or HIPAA record-retention rules. Crucially, Object Lock settings should be replicated across sites (if multi-site is enabled), ensuring your retention policy survives any failover.

Auditing & Monitoring

All object API calls should be able to be logged for audit. Preferability, these logs should be able to be streamed to monitoring systems. On-prem deployments often use tools like Prometheus, ELK for observability. This way, every read/write is traceable for compliance reviews.

Together, these controls mean that data in the lakehouse is always governed: only authorized principals access PII or PHI; data-at-rest is encrypted; and tamper-proof logs and WORM protection satisfy regulations like GDPR’s “right to be forgotten” controls and HIPAA’s data integrity and access rules.

Apache Iceberg for Table Metadata and Analytics

The heart of a data lakehouse is an open table format (OTF). There are three current standards: Apache Iceberg, Apache Hudi and Delta Lake. Although Apache Iceberg has by and large stood out, all of these OTFs organize raw files into queryable tables.

Apache Iceberg maintains metadata (manifests, partition indices, snapshots, etc.) in the object store alongside the data files. This metadata enables advanced features: atomic commits, time travel, schema and partition evolution, branching, and hidden partitioning. Any SQL engine or analytics tool (Spark, Flink, Trino, Dremio, Snowflake, etc.) that speaks Iceberg can read the same tables concurrently, without locking or pre-copies. Iceberg’s multi-engine compatibility ensures you “pick your favorite tools” while the data remains in one place, providing data engineering teams with unprecedented flexibility compared to traditional database solutions.

Choosing the right object storage for your data lakehouse can have concrete benefits. Object storage that is both durable and built for sale means that that Iceberg metadata can be reliably persisted. When coupled with S3-over-RDMA for ultra-low-latency, high-throughput reads,your Iceberg tables can be served to GPU/CPU clusters at near‑local-disk speeds, which is critical for AI workloads. The combined platform, compute Apache Iceberg and object storage delivers a true lakehouse: flexible, high-performance and secure.

Iceberg Catalog-Level Governance & Access Control

Iceberg catalogs can be a central part of a robust data lakehouse security plan. One way that can happen is by leveraging centralized role-based access control (RBAC) through lakehouse catalogs, organizations can precisely manage permissions and security.

Lakehouse catalogs like Apache Polaris and Nessie have this critical capability. Polaris provides comprehensive RBAC with service principals, role assignments, and detailed privileges like TABLE_READ_DATA, alongside credential vending for short-lived access tokens, significantly reducing security risks. Nessie, in turn, supports a Git-style governance approach, offering granular access control per branch or table path, making it ideal for managing versioned data access.

Additionally, the Iceberg REST Catalog enhances security by providing temporary, scoped S3 credentials for each table access. This practice ensures least-privilege access and minimizes the exposure window for potential security incidents. Furthermore, following Iceberg Java 1.6+, the built-in OAuth token endpoints have been deprecated due to security considerations. Instead, it is recommended that Iceberg catalogs integrate with external identity providers, such as Okta or Cognito, facilitating secure token passing via standard headers. Collectively, these features reinforce Iceberg’s position as a secure and compliant cataloging solution within the modern data lakehouse.

Why MinIO AIStor is the Logical Choice

MinIO AIStor was engineered with a security-first design to meet the demands of modern AI and analytics workloads. It offers end-to-end, zero-knowledge encryption/ Which means data is encrypted in transit (TLS 1.3) and at rest (AES-256), with server-side encryption that integrates with external key management systems (KMS) for full compliance with enterprise security policies. Multi-user, multi-tenant isolation is enforced through strict namespace separation, with full support for regulatory requirements like HIPAA and GDPR.

All operations are logged and auditable, and administrators can configure object locking, legal hold, and WORM (Write Once Read Many) protection for data immutability and governance. Despite this strong security posture, AIStor does not compromise on performance. It saturates 100GbE networks and consistently exceeds Amazon S3 throughput, even when deployed to EKS. It runs on standard, commodity hardware (NVMe drives, JBOFs, 10-100GbE switches) with a lightweight 100 MB binary footprint and no external dependencies.

Crucially for data sovereignty, AIStor can run in your data centers or colos (on bare metal or Kubernetes), keeping all control in-house. For hybrid architectures, AIStor also deploys seamlessly to public clouds, giving you full portability without vendor lock-in.

When you compare on-prem and hybrid object stores, very few meet this bar: security, speed, scale, and simplicity. AIStor is in a class of its own.

Data Security for Data Lakehouses

In summary, modern on-prem/hybrid lakehouses need a secure, S3-compatible, cloud‑native object store and MinIO AIStor delivers it. It unifies durability, security, and governance out of the box, so you can focus on extracting value from data rather than wrestling with storage. For technical leaders architecting next‑generation data platforms, AIStor represents the low-TCO, high-flexibility foundation for a truly cloud-native data lakehouse solution (just without the public cloud).