Move AI from pilotto production with confidence
KLA adds runtime guardrails, human approvals, and audit-ready evidence between your AI agents and critical systems, so engineering can ship faster while Risk and InfoSec stay aligned.
Intercept
The agent action is caught at runtime before any irreversible damage is done.
Decide
A policy checkpoint evaluates the risk and routes to the appropriate human approver.
Prove
Every decision provides a full evidence trail for compliance and audit.
Live intercept
Decision-time controls in the execution path
Agent action
Treasury copilot requests `wire_transfer.create` for EUR 250,000
The operational loop for governed AI
Four stages keep autonomous AI under control from first action to final audit: set the rules, run with guardrails, watch for drift, and prove what happened.
Govern
Decision routing
Insert policy-as-code checkpoints to block rogue tool calls and route high-stakes actions to named reviewers.
Operate
Least-privilege execution
Run governed AI in the real execution path with runtime controls, release discipline, and operator visibility.
Assure
Continuous assurance
Monitor drift, quality, fairness, and threshold alerts in one operating surface before issues become incidents.
Prove
Cryptographically sealed evidence
Generate signed execution lineage and evidence bundles that map runtime controls directly to frameworks and internal controls.
One layer between autonomous AI and your critical systems
KLA does not replace your agent framework. It gives existing workflows a steady control plane.
Live intercept path
Intercept agent tool calls, evaluate policies, and route for approvals seamlessly within a single execution path before downstream systems are touched.
Learn morePolicy-as-code checkpoints
Express risk guardrails, identity thresholds, and tool-level constraints as code, enforcing them consistently across all your deployment environments.
Learn moreHuman oversight routing
Dynamically escalate high-stakes decisions to the correct reviewers via Slack or internal queues without halting the main engineering workflow.
Learn moreGovern in place
Layer KLA over existing logic with native SDKs and OpenTelemetry rather than ripping out and replacing your current AI agent framework.
Learn moreProvable compliance
Every governed decision automatically generates signed lineage that maps directly to your auditing and trust frameworks — no extra documentation step required.
Learn moreProductize the pilot that usually gets stuck
The fastest way to prove KLA is to govern one workflow your internal reviewers already care about. The deliverable is not a slide deck—it is a governed execution path.
Connect one real workflow
Instrument the existing agent, API, or workflow you already want to ship. No rewrite, no platform migration, no design-lab detour.
Define decision-time controls
Turn business rules, security requirements, and approval thresholds into policy-as-code checkpoints.
Run live traffic with approvals
Observe real decisions, route high-stakes actions to humans, and tune intercepts against near misses.
Hand over proof and rollout plan
Leave with governed execution metrics, approver workflows, and a signed lineage package your internal stakeholders can trust.
Governed workflows for every regulated industry
The bottleneck changes by sector, but the pattern is the same: an AI workflow reaches a high-stakes decision, a reviewer asks how it is controlled, and the rollout stalls. KLA gives that workflow an execution path the business can trust.
Financial services
- Stop unapproved trades, payout changes, and treasury actions before they hit core systems.
- Escalate high-value decisions to named approvers with full execution context.
- Produce lineage that internal controls, model risk, and audit teams can all use.
Insurance
- Keep claims AI from settling, denying, or escalating without the right review path.
- Govern underwriting recommendations with approval thresholds and evidence capture.
- Show adjusters and regulators exactly how the recommendation was generated.
Healthcare
- Prevent clinical copilots from issuing unapproved recommendations or disclosing PHI.
- Insert maker-checker review into discharge, diagnosis support, and care coordination flows.
- Retain the execution context investigators and quality teams will ask for later.
Pharma
- Apply continuous validation controls to regulated content, quality, and lab workflows.
- Keep model changes, approvals, and release decisions tied to execution evidence.
- Scale governed automation without turning QA into a bottleneck.
Government
- Require review for citizen-facing recommendations, eligibility outcomes, and notices.
- Create clear decision lineage for oversight, appeals, and internal accountability.
- Govern agency AI in place without forcing a full re-platform of existing systems.
From compliance mapping to production controls
Practical guides for platform, security, and risk teams ready to move from regulatory checklists to working governance.
Human approval escalation playbook
A practical guide to adding maker-checker controls to AI decisions without slowing the entire workflow.
Execution lineage guide
What to capture when an AI workflow acts on a real system and you need proof later.
Deployment patterns for existing stacks
See the difference between governing in place with SDKs and routing execution through KLA.
Ship faster with governance built in
Start with one workflow. In four weeks, have a governed execution path that your platform, security, and audit teams can all inspect.
Your real workflow, not a canned demo.