Cybersecurity
Data Security & Governance
Overview
Security that ignores the data platform misses the crown jewels. We bring our data engineering depth to protecting the data itself.
Outcomes we measure
- Sensitive data discovered and classified
- Datasets with fine-grained access policies
- Encryption and key management coverage
- Data-exfiltration paths closed
Problems we solve
Sound familiar?
- 01
Copies of customer and health data sit in analytics sandboxes nobody owns.
- 02
Everyone with warehouse access can see everything.
- 03
AI pipelines are being fed documents that include sensitive personal data.
What we deliver
Concrete deliverables, not slideware.
Discovery and classification
Automated discovery of sensitive data across platforms, with a practical classification scheme.
Access controls
Row- and column-level security, masking and attribute-based policies on your data platforms.
Encryption and keys
Encryption at rest and in transit, customer-managed keys and key rotation.
DLP and monitoring
Data-loss prevention policies and monitoring for unusual access, including AI and RAG pipelines.
How it works
From first call to production.
- 01
Discover
Find and classify sensitive data across sources.
2–3 weeks
- 02
Design policies
Agree access model, masking and retention with data owners.
1–2 weeks
- 03
Implement
Apply controls on platforms, pipelines and SaaS.
4–10 weeks
- 04
Monitor
Access monitoring, reviews and policy tuning.
Ongoing
Tech & frameworks
Tools we use — in your stack.
- Microsoft Purview
- Unity Catalog
- Snowflake Horizon
- Immuta
- AWS Macie
- Google Cloud DLP
- AWS KMS
- Azure Key Vault
Related projects
Where we've done this.
Case studies
Case studies for this service are being prepared. Ask us for references on a call.
FAQ
Frequently asked questions
Cybersecurity
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Scope it with a senior practitioner
Book a 45-minute security readiness call.
Where you are, what to fix first, and what it would take.