Secure Cloud &
Data Engineering
Build secure cloud and data platforms from the start. Cloud and data environments support analytics, reporting, integrations, AI workloads, customer systems and business decision-making. If security isn't built in early, organisations end up with exposed data, excessive permissions, weak logging, unclear ownership and poor governance. RTCS designs, implements and reviews cloud data work with security built into each layer.
- Cloud data platform design across Azure, AWS and Google Cloud
- Secure data pipeline and integration implementation
- Data lake and warehouse security review
- Microsoft Fabric, Power BI and analytics security
- Data access control, classification and governance
- Secure API design, secrets handling and identity
- Logging, monitoring, backup and recovery design
- AI and analytics data readiness review
- Risk-rated findings with remediation roadmap
Data platforms become business-critical fast.
Secure cloud and data engineering brings security into the design, build and operation of cloud data platforms - architecture, identity and access, data protection, logging, network controls, configuration, governance, backup planning and secure integration. The goal is data platforms that are useful, scalable and properly controlled.
Where data platforms usually expose risk.
Sensitive data stored without clear controls
Too many users with broad access
Weak separation between dev and production
Publicly exposed storage or services
Poor logging of data access
Pipelines running with excessive permissions
Unclear ownership of datasets
Inconsistent retention and deletion practices
Weak backup and recovery planning
Analytics tools exposing more data than intended
AI workloads using data without governance
Cloud data platforms designed and reviewed for security.
RTCS designs or reviews secure cloud data platforms across Azure, AWS and Google Cloud - including data lakes, warehouses, storage, databases, networking, encryption, key management, logging, identity and environment separation.
For broader cloud configuration assurance, pair with Cloud Security. For Azure-specific CIS Benchmark work, see Azure Security Posture & CIS Benchmark Review.
Access aligned to business need. Reviewed regularly.
RTCS reviews and improves data access governance across users, groups, service accounts, applications and analytics platforms.
Pair with Identity & Access Management for the broader identity controls that govern who reaches the data in the first place.
Pipelines & Analytics
Secure Data Pipelines & Integrations
Pipeline identity and permissions, API security, secret management, secure data transfer, error handling, logging and alerting, dev / prod separation, change control and monitoring. The pipelines that move data between systems shouldn't be the weakest link.
Analytics & Reporting Security
Power BI, Microsoft Fabric and cloud analytics platforms - workspace access, report sharing, dataset permissions, external sharing controls, row-level security, refresh permissions, gateway configuration, audit logging and sensitive-data exposure.
AI depends on data quality, access control and governance.
RTCS helps prepare data environments for AI and analytics use cases without losing control of sensitive information.
For LLM application testing, prompt injection, RAG and Copilot governance, pair with AI Security & Consulting.
This vs Cloud Security
Cloud Security
Broader cloud security review across identity, network, exposure, logging and platform configuration. See Cloud Security.
Secure Cloud & Data Engineering
Focused specifically on cloud data platforms, pipelines, analytics, integrations and data governance - and on building them, not just assessing them.
Understand to Improve
A practical, staged engagement that designs and delivers secure data platforms - not retrofits security once something breaks.
Review business requirements, data flows, cloud platforms, security risks and current architecture.
Develop or review secure architecture, access models, data controls and implementation requirements.
Support secure build activities across cloud, data pipelines, storage, analytics and integrations.
Test configuration, access, logging and data handling controls.
Provide practical recommendations, documentation and a roadmap for ongoing uplift.
Who It's For & What You Receive
Who This Service Is For
- Building or improving cloud data platforms
- Use Azure, AWS or Google Cloud for data workloads
- Use Microsoft Fabric, Power BI or other analytics
- Need secure data pipelines or integrations
- Need to control access to sensitive data
- Need better data governance
- Preparing data for AI or analytics
- Need to reduce cloud data exposure
- Want secure implementation without complexity
Typical Deliverables
- Secure cloud data architecture review
- Data platform security assessment
- Data access control review
- Secure pipeline design
- Cloud configuration findings
- Analytics platform security review
- AI data readiness review
- Risk-rated recommendations
- Implementation plan
- Technical documentation
- Governance roadmap
- Executive summary
Where data engineering connects to the rest of the program.
Cloud Security →
Broader cloud configuration, identity, exposure and governance review across the same environments.
Azure Security Posture & CIS Benchmark →
CIS-aligned configuration review for Azure subscriptions hosting data platforms.
Identity & Access Management →
Entra ID, RBAC and privileged access governing who reaches the data.
AI Security & Consulting →
LLM testing, prompt injection, RAG security and Copilot governance for AI workloads.
Privacy Advisory →
Privacy Impact Assessments and APP / WA PRIS Act handling for personal information.
IT Project Implementation →
Delivery support for the build, migration and operational handover phases.
Disaster Recovery & Backups →
Recovery design and backup resilience for the data platforms you depend on.
Governance, Risk & Compliance →
Evidence, audit alignment and control mapping across ISO 27001, Essential Eight and more.
Data platforms should be useful, reliable and properly controlled. Talk to us about secure cloud data architecture, data platform security, secure data pipelines, analytics security or AI data readiness.
Common Questions
What is secure cloud and data engineering?
Secure cloud and data engineering is the design, implementation and review of cloud data platforms, pipelines and analytics environments with security built in from the start.
Which cloud platforms do you support?
RTCS can support Azure, AWS and Google Cloud environments.
Can you review Power BI or Microsoft Fabric security?
Yes. RTCS can review access, sharing, dataset permissions, row-level security, workspace controls, gateways and audit logging.
Can you help secure data pipelines?
Yes. RTCS can review pipeline permissions, API integrations, secrets, logging, monitoring and secure transfer controls.
Can you help with AI data readiness?
Yes. RTCS can review whether data used for AI is properly governed, classified, controlled and monitored.
Can you help implement changes?
Yes. RTCS can support implementation, remediation, documentation and secure configuration changes.
Is this the same as cloud security?
No. Cloud security is broader. Secure cloud and data engineering focuses specifically on cloud data platforms, pipelines, analytics, integrations and data governance.
Not sure what you need?
Use the enquiry form or email [email protected] with a brief summary. Include your organisation, the service area if known, and any timing requirements.