CASA explores AI for digital asset operations under strict onshore controls
The Civil Aviation Safety Authority is assessing AI-enabled digital asset management systems (DAMS) as it plans to replace its current platform. The focus is on practical AI features that speed up findability and reduce manual effort, while keeping all processing and storage within Australia. CASA also wants its existing operational processes preserved in the new system, not reset from scratch.
What CASA is asking for
In its request for information, CASA asks vendors to demonstrate AI embedded in their platforms and the measurable benefits it delivers. Capabilities of interest include:
- Auto-tagging and metadata extraction
- Face, logo, and object recognition
- Speech-to-text for video and audio
- Text-in-image detection (OCR)
- Semantic search (natural language search)
- Auto smart collections
- Visual similarity search
- Duplicate and near-duplicate detection
The current DAMS supports 10 full-access users and 500 non-contributor users. Any successor platform must handle this mix cleanly and scale without breaking permissions or existing workflows.
Security, sovereignty, and access
CASA requires all data records, user information, and analytic outputs to be stored, processed, and generated within Australia. It also states a preference for AI components to operate in an environment that is closed to CASA, rather than relying on external processing paths.
The RFI seeks details on capability, compliance, cost, and suitability to help select a system that meets operational, security, and regulatory needs. Don't expect commentary from the authority while the RFI remains open.
Why this matters for operations
AI in DAMS can cut time spent on tagging, accelerate search, and improve asset reuse. For operations teams, the value appears in throughput, accuracy, auditability, and predictable costs-without adding risk to data sovereignty or access controls.
The real test is whether AI features work at your scale, with your permissions model, and across your existing processes. That's where proofs of concept and measurable acceptance criteria become essential.
Questions to press vendors on
- Data residency: Can they guarantee all data and derived analytics remain in Australia, including logs and embeddings?
- AI processing path: Are models hosted onshore? Any sub-processors? Clear data flow diagrams and contracts?
- Closed processing: Can AI features run in an environment isolated to the authority with no external training or tuning on your content?
- Privacy: How are faces, voices, and sensitive text handled? Controls to disable or scope detection per collection?
- Accuracy: Benchmarks for auto-tagging, OCR, speech-to-text, and similarity matching. How are false positives corrected and learned from?
- Governance: Audit trails for every AI action, including who approved tags, edits, and deletions. Tamper evidence and exportable logs.
- Access control: Role-based permissions, attribute-based policies, and inheritance across collections. Support for SSO and MFA.
- Performance: Indexing throughput, search latency at peak, and SLA targets for AI jobs.
- Content lifecycle: Retention, legal hold, versioning, and redaction tools that work with AI outputs.
- Integration: APIs and event hooks for MDM, records management, and workflow tools. Rate limits and cost model for API-heavy use.
- Cost clarity: How pricing scales for storage, users, API calls, AI jobs, and onshore hosting. Caps and forecasting tools.
Migration and change plan
- Inventory and classify current assets, owners, and permissions; clean up duplicates before migration.
- Map existing metadata to the new schema; define what AI will generate vs. what users must maintain.
- Run a pilot with a representative dataset (images, video, audio, documents) and locked acceptance criteria.
- Validate accuracy: benchmark OCR, transcription, tagging, and similarity search. Track error rates and correction speed.
- Stand up an access matrix that mirrors your current model; test inheritance and exception cases.
- Define SLAs, runbooks, and rollback steps. Include monitoring for AI job queues and indexing health.
- Train users on review workflows, especially how to correct AI output and escalate sensitive cases.
Metrics that prove value
- Time-to-find: median search time per query and reduction against baseline
- Manual tagging hours saved per month
- Precision/recall for auto-tagging, OCR, and transcription on a fixed test set
- Duplicate detection accuracy and rework avoided
- Indexing throughput and average queue time for AI jobs
- Policy compliance: percentage of assets fully tagged with required fields
- User adoption: weekly active users, query success rate, and satisfaction scores
Standards and guidance
For teams building requirements and controls, these resources are useful:
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