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SOLUTION 06
AI SAFETY & GOVERNANCE
Keeping AI safe means giving models governed, protected data without exposing sensitive fields or losing track of where that data came from. CCA builds secure data foundations, protected pipelines and auditable lineage, so organisations can deploy AI on trusted enterprise data while still answering regulators and auditors about what informed each decision.
Trapped Between Two Undesirable Choices
Ungoverned AI Amplifies Risk. Locked-Down Data Stalls Innovation.
The choice: Deploy AI Now, Govern It Later
What it means: Move fast against existing data, and plan to bolt on security, lineage and oversight once the model is already in production.
Why it fails
AI Without Secure Foundations
- Amplifies existing data risk instead of reducing it
- Exposes sensitive fields to prompt injection and model poisoning
- Synthetic identity and deepfake fraud go undetected
- No lineage to trace what data trained or informed a model
The choice: Lock Data Down, Delay AI
What it means: Restrict access to sensitive data so tightly that analytics and AI initiatives stall indefinitely, while pressure to innovate keeps building.
Why it fails
Locking Data Down Entirely
- Analytics and AI initiatives stall indefinitely
- Business value from existing data assets goes unrealised
- Teams build workarounds to get the access they need anyway
- Board and regulator pressure to innovate goes unanswered
A Secure-Foundations AI Strategy
CCA and OpenText combine discovery, protection and auditable lineage so AI systems can safely draw on enterprise data - with governance built in from day one.
Secure Data Foundations Enable Safe AI
AI safety begins with data protection architecture, not model governance alone - sensitive data must be discovered, classified, encrypted and governed before it reaches an AI workflow.
Auditable data lineage means AI teams always know the source of the data feeding their models.
Significantly Reduces AI Risk
- Encryption and tokenisation let AI consume data without exposing raw values
- Automated discovery and classification cover legacy and modern platforms alike
- Governed, policy-controlled access keeps AI systems operating on trusted data
- Auditable lineage supports explainability and regulatory assurance
Safe AI, End to End
Discovery, protection and lineage enforced before data ever reaches a model.
Enterprise Data
Legacy & modern platforms
Discover, Protect & Trace
Classification, encryption, lineage
AI & Analytics
Trusted, governed inputs
Purpose-Built Components, Working as One
Rocket Data Replicate and Sync (RDRS)
Real-time change data capture and synchronisation across legacy and modern environments, as changes occur.
OpenText Voltage SecureData
Encryption, format-preserving encryption, tokenisation and policy-driven controls - without impacting usability.
CCA Secure Data Connector
Integrates real-time replication with protection services so sensitive data stays protected throughout synchronisation.
OpenText Voltage Data Security Platform (DSP)
Discovers and automatically classifies structured data assets - what exists, where it resides, and how it should be governed.
Rocket Data Intelligence, Insight and AI
Auditable data lineage integrated with RDRS - so teams always know the source of the data feeding their AI models.
What the Solution Delivers
Data Discovery & Classification
Automated identification of sensitive structured data across legacy and modern platforms.
Encryption & Tokenisation for AI
Lets models and analytics consume protected data without exposing raw values.
Auditable Data Lineage
Traces the source of every dataset feeding an AI workflow, supporting explainability.
Governed, Policy-Controlled Access
Keeps AI systems operating on trusted, compliant data assets.
Outcomes That Matter to the Business
Build Secure Foundations Before Scaling AI
Make Enterprise Data Available Without Exposing It
Support Explainable, Auditable AI
Reduce New AI-Driven Attack Surfaces
Maintain Public & Regulator Trust
Frequently asked questions
What does it mean to keep AI safe?
It means giving AI models governed, protected data without exposing sensitive fields or losing track of where that data came from. AI safety begins with data protection architecture rather than model governance alone.
Do we have to lock data down to keep AI safe?
No. Locked-down data stalls innovation while ungoverned AI amplifies risk. The approach is to build secure foundations instead: CCA and OpenText combine discovery, protection and auditable lineage so AI systems can draw on enterprise data safely, with governance built in rather than added afterwards.
Can AI use sensitive data without exposing it?
Yes. Encryption and tokenisation let AI consume data without exposing raw values, and governed, policy-controlled access keeps AI systems operating on trusted data.
Does this cover legacy and mainframe data?
Yes. Automated discovery and classification cover legacy and modern platforms alike, so data originating in mainframe or distributed systems is classified, protected and traceable before it reaches a model.
How do we know where a model’s data came from?
Auditable data lineage means AI teams always know the source of the data feeding their models. Rocket Data Intelligence provides lineage integrated with Rocket Data Replicate and Sync, and that audit trail supports explainability and regulatory assurance.
When does governance apply, before or after the model?
Before data ever reaches a model. Discovery, protection and lineage are enforced up front, with sensitive data discovered, classified, encrypted and tokenised as it moves from enterprise systems into AI and analytics.
Build AI foundations that are safe today, and ready for tomorrow.
Talk to a consultant about embedding secure data architecture into your AI roadmap.
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