Responsible AI
Build accountability into everyday AI.
Human-led by design
Keep People and Controls in Place
Build in appropriate access, human review, governance and measurement so new capabilities have clear boundaries and accountable owners.
Responsible deployment requires clear boundaries for data, decisions, human review and ongoing monitoring. The right controls depend on the workflow and its consequences.
A practical control model
Clear boundaries. Accountable people.
Named owners retain authority
Approved information
What may the system access?
Defined actions
What may it do without approval?
Quality & risk checks
When should it defer to a person?
Operating controls
Who can pause or stop the workflow?
Before launch
Make the boundaries explicit.
- Assign business and technical owners
- Identify approved data, access and retention requirements
- Evaluate vendors, models and realistic failure cases
- Set human approval points and escalation paths
- Agree success measures, incident procedures and stop controls
In operation
Keep oversight active.
- Monitor output quality, reliability and operating cost
- Review exceptions and report failures
- Maintain activity records appropriate to the workflow
- Train users on responsibilities and limitations
- Reassess controls when data, tools or scope change
Shared responsibility
Work with the people who own the risk.
Work with your security, risk, legal and compliance teams to establish appropriate AI policies and controls. Address sensitive data, access permissions, vendor and model evaluation, testing, oversight and incident procedures. Keep accountability and approval authority clearly assigned.
These are planning considerations, not a certification or guarantee of regulatory compliance. Your authorized security, legal, risk and compliance professionals determine the requirements for your organization.
Your next step
Plan the controls alongside the capability.
Explore how human approval, testing and operational ownership fit into our delivery approach.