Trustworthy AI starts
with trusted data.
The data layer we have hardened for years in production — databases, pipelines, access control — now guarding what feeds your models.
The layer everything stands on.
AI did not make data security obsolete — it multiplied it. Data that once sat safely inside a database now flows into prompts, embeddings, logs and third-party APIs. Every retrieval corpus is a fresh copy of your crown jewels with a new audience.
We have spent years running PostgreSQL platforms and streaming pipelines in production — that is the DoThinkData practice. DoThinkSecure applies the same field experience to the data boundary of AI systems.
Database Security Hardening
PostgreSQL-centric hardening from a team that runs it in production — not a checklist, a discipline.
What we do
- Authentication and TLS everywhere; SCRAM and certificate rotation
- Roles, row-level security and least privilege down to schema level
- Audit logging (pgaudit) with a review cadence that fits your team
- Configuration and patch discipline; upgrades without downtime
- Secrets hygiene: no credentials in configs, prompts or agent toolbelts
Deliverables
- A hardening assessment with priorities
- Applied configuration changes, reviewed and reversible
- An audit-log review rhythm
Encryption & Key Management
Encrypted at rest, in transit and — increasingly — in use. Keys held by process, not by people.
What we do
- Encryption at rest and in transit, end to end
- Column-level encryption and tokenization for PII
- Key management: generation, rotation, separation of duties
- Envelope encryption for multi-tenant and AI workloads
- Data masking for non-production and analytics copies
Deliverables
- An encryption design that fits your stack
- A key management runbook
- Masked staging pipelines — refreshable, like everything else we build
Secure Data Integration
Change-data-capture and streaming pipelines (Debezium, Kafka Connect) that stay healthy, trustworthy and observable.
What we do
- Least-privilege credentials and segmentation between trust zones
- Integrity checks and reconciliation at every hop
- Health monitoring with alerting — pipelines that fail loudly, not silently
- Schema governance and safe evolution
- Our own Java wrapper exposes Debezium / Kafka Connect health as JMX metrics — battle-tested in production
Deliverables
- A pipeline security review
- Monitoring wrapper deployment and dashboards
- Incident runbooks for pipeline failures
Securing the AI Data Boundary
What may enter a prompt is a security decision, not an accident. This is where data security and LLM security meet — and where most AI incidents are actually prevented.
What we do
- Classification: what data may reach models, embeddings and third-party APIs
- Minimization and masking before retrieval or embedding
- Chunk-level access control aligned with source-of-truth permissions
- Ingestion provenance and poisoning prevention for corpora
- Vector store hardening and retention policy
Deliverables
- An AI data boundary policy your teams can follow
- Applied masking and access controls
- Corpus provenance and poisoning checks
Know where your data goes.
We will map every path your data takes into AI systems — then close the ones you did not choose.
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