Control index

DASF-1 — SSO with IdP and MFA
DASF-2 — Sync users and groups
DASF-3 — Restrict access using IP access lists
DASF-4 — Restrict access using private link
DASF-5 — Control access to data and other objects
DASF-6 — Classify data
DASF-7 — Enforce data quality checks on batch and streaming datasets
DASF-8 — Encrypt data at rest
DASF-9 — Encrypt data in transit
DASF-10 — Version data
DASF-11 — Capture and view data lineage
DASF-12 — Delete records from datasets
DASF-13 — Use near real-time data
DASF-14 — Audit actions performed on datasets
DASF-15 — Explore datasets and identify problems
DASF-16 — Secure model features
DASF-17 — Track and reproduce the training data used for ML model training
DASF-18 — Govern model assets
DASF-19 — Manage end-to-end machine learning lifecycle
DASF-20 — Track ML training runs
DASF-21 — Monitor data and AI system from a single pane of glass
DASF-22 — Build models with all representative, accurate and relevant data sources
DASF-23 — Register, version, approve, promote, deploy and monitor models
DASF-24 — Control access to models and model assets
DASF-25 — Use retrieval augmented generation (RAG) with large language models (LLMs)
DASF-26 — Fine-tune large language models (LLMs)
DASF-27 — Pretrain a large language model (LLM)
DASF-28 — Create model aliases, tags and annotations
DASF-29 — Build MLOps workflows
DASF-30 — Encrypt models
DASF-31 — Secure model serving endpoints
DASF-32 — Govern and monitor access of AI model and model serving endpoints
DASF-33 — Manage credentials securely
DASF-34 — Run models in multiple layers of isolation
DASF-35 — Track model performance
DASF-36 — Set up monitoring alerts
DASF-37 — Set up inference tables for monitoring and debugging models
DASF-38 — Platform security — penetration testing, red teaming, bug bounty and vulnerability management
DASF-39 — Platform security — Incident Response Team
DASF-40 — Platform security — internal access
DASF-41 — Platform security — secure SDLC
DASF-42 — Employ data-centric MLOps and LLMOps
DASF-43 — Use access control lists
DASF-44 — Triggering actions in response to a specific event
DASF-45 — Evaluate models
DASF-46 — Store and retrieve embeddings securely
DASF-47 — Compare LLM outputs on set prompts
DASF-48 — Use hardened Runtime for Machine Learning
DASF-49 — Automate LLM evaluation
DASF-50 — Platform compliance
DASF-51 — Share data and AI assets securely
DASF-52 — Source code control
DASF-53 — Third-party library control
DASF-54 — Implement AI guardrails
DASF-55 — Monitor audit logs
DASF-56 — Restrict outbound connections from models
DASF-57 — Use attribute-based access controls (ABAC)
DASF-58 — Protect data with filters and masking
DASF-59 — Use clean rooms
DASF-60 — Rate limit number of inference queries
DASF-61 — Train users on AI risk taxonomy and AI/ML security
DASF-62 — Implement network segmentation
DASF-63 — Update software
DASF-64 — Limit access from AI models and agents
DASF-65 — Implement end-to-end AI traceability
DASF-66 — Use Human-in-the-loop feedback
DASF-67 — Federate authentication
DASF-68 — Use Securely Hosted Managed MCP Servers
DASF-69 — Securely host Custom MCP Servers
DASF-70 — Securely connect to External MCP Servers
DASF-71 — Log and register AI agents
DASF-72 — Securely store and reuse agent state
DASF-73 — Register prompts