Control mapping should start
from governed execution
KLA is not a replacement for every governance system-of-record in the enterprise. It is the runtime layer that captures what your AI actually did so internal controls, trust teams, and external frameworks have something real to map against.
How control mapping is organised
Runtime controls come first. Once KLA governs an action, the resulting lineage maps to internal controls and external frameworks automatically.
EU AI Act
Use KLA to capture runtime evidence for logging, oversight, risk management, and technical documentation workflows.
- Article 12 logging and traceability support
- Article 14 human oversight implementation patterns
- Annex IV and Article 17 evidence mapping as a downstream step
GDPR and privacy controls
Prevent unsafe data movement and preserve the runtime record needed to understand what the AI actually accessed and did.
- Policy checkpoints on outbound data movement
- Execution context for access review and investigation
- Retention and residency controls for governed workflows
Sector-specific controls
Translate regulated workflow expectations into runtime controls for sectors like finance, insurance, healthcare, and government.
- Threshold-based approvals and maker-checker flows
- Signed lineage for review, audit, and appeals
- Separation of runtime governance from static system-of-record tooling
Operational playbooks
Step-by-step guides that bridge regulatory requirements and day-to-day engineering work.
Execution lineage pack
Templates and artifacts for explaining what happened in a governed workflow after the fact.
Human approval playbook
A practical path to implementing decision-time human review without slowing every workflow.
Quality-system operating pack
Operational guidance for building repeatable governance processes around regulated AI workflows.
Need a security review or trust package?
Route to the right conversation and keep the evaluation tied to the workflow you are actually trying to deploy.