AI governance
How the organization assigns ownership, oversight, accountability, policy, and decision-making around AI systems.
- Who is responsible?
- How are risks reviewed?
- How are decisions documented?
- How are policies updated?
THE AGENTICERT FRAMEWORK
A practical framework for clearer AI safety communication.
The Agenticert Framework helps organizations think through, organize, and explain important AI safety, governance, security, and responsible deployment practices.
AGENTICERT / THE AGENTICERT FRAMEWORKEST. 2026 · GLOBAL
It is not a legal standard or formal certification. It is a practical transparency and review framework designed to support better communication and improvement.
Data privacy
How the organization handles data collection, data flow, user information, retention, privacy commitments, and vendor relationships.
- What data is processed?
- Where does data go?
- How long is it retained?
- Which providers are involved?
Model security
How the organization protects AI systems from misuse, abuse, leakage, prompt injection, unauthorized access, and other security risks.
- Interaction protection
- Abuse controls
- Sensitive outputs
- Access management
Human oversight
How humans remain involved in important decisions, escalation, review, approval, or intervention.
- When is review required?
- How are issues escalated?
- Who can stop a system?
- How is oversight documented?
Transparency
How the organization explains AI use, system limitations, user expectations, and important risks.
- AI involvement
- Clear limitations
- Understandable policies
- Meaningful context
Bias and fairness
How the organization considers bias, unfair outcomes, representational harms, evaluation gaps, and affected users.
- Bias testing
- Fairness concerns
- Complaint review
- Documented changes
Testing and monitoring
How the organization evaluates performance, monitors behavior, tests updates, detects failures, and reviews systems over time.
- Pre-deployment tests
- Ongoing monitoring
- Issue detection
- Change review
Incident response
How the organization handles AI-related failures, harmful outputs, misuse, user complaints, disclosure, escalation, and remediation.
- Incident reporting
- Investigation ownership
- User communication
- Post-incident action
User protection
How the organization protects users from harm, confusion, misuse, unsafe reliance, or unclear expectations.
- User safeguards
- Warnings and limits
- Vulnerable users
- Complaint handling
Responsible deployment
How the organization decides when, where, and how AI systems should be launched, limited, monitored, updated, or withdrawn.
- Release process
- Risk review
- Fallback plans
- Deployment learning
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