SOP-1050-01-AI_AI-Security-Administration-and-Governance
Title: AI Security Administration and Governance
| Effective Date | Version | Previous Versions | Reason for Update |
|---|---|---|---|
| YYYY-MM-DD | 1.0 | None | New SOP |
1. Objective
This SOP defines the policies and procedures for managing and administering security for AI-based systems and resources within the organization. It ensures that AI-related data, models, and infrastructure are protected against unauthorized access, misuse, or compromise, and that all security practices comply with relevant legal, regulatory, and ethical standards, including AI IRB review.
2. Scope
- This SOP applies to all AI projects, systems, and personnel within the organization, as well as any third-party vendors or collaborators who interact with or manage our AI environments.
- Covers physical, network, infrastructure, and application-level security measures that govern the AI pipeline (data acquisition, data storage, model development, model deployment, and post-deployment monitoring).
- Addresses security protocols for cloud-based or on-premise AI solutions.
3. Applicable To
- AI Project Sponsor
- AI IRB Liaison (AI IRB mandated oversight)
- Security Administration Team
- AI Development Team
- Quality Assurance Team
- Operations Team
- Data Science / AI Engineering Team
- All Employees and Contractors handling AI data or systems
4. References & Related Procedures
- SOP-1000-01-AI: AI Program/Project Management
- SOP-1001-01-AI: Document Governance
- SOP-1040-01-AI: Requirements Definition
- SOP-1041-01-AI: Detail Design
- SOP-1200-01-AI: Development
- SOP-1210-01-AI: Quality Function
- Regulatory/Compliance references (GDPR, HIPAA, ISO 27001, NIST frameworks, etc.)
- AI IRB guidelines for ethical compliance
5. Roles and Responsibilities
| Role | Responsibility |
|---|---|
| AI Project Sponsor | Provides overall direction and ensures sufficient resources for security measures. |
| AI IRB Liaison | Coordinates with AI IRB for any new AI security approvals or changes that might have ethical/regulatory impact. |
| Security Administrator | Implements and maintains security controls for all AI environments, manages user accounts, reviews logs, oversees password policies. |
| AI Development Team | Adheres to secure coding standards, escalates vulnerabilities to Security Administrator. |
| Data Science / AI Engineering Team | Ensures data usage, model training, and deployment align with security best practices, engages Security Administrator for critical changes. |
| Operations | Provides infrastructure security, manages environments (dev, QA, production), implements patching and monitoring. |
| Quality Assurance | Verifies security compliance in test phases, coordinates vulnerability scans. |
| Technical Support | First-level triage for security-related AI support requests, escalates issues to the Security Administrator. |
6. Definitions
- AI IRB: Internal Review Board specifically for reviewing AI systems’ ethics, compliance, and risk.
- Security Administrator: The individual/team responsible for user access, system monitoring, and implementing security policies for AI systems.
- Access Profile: The permitted level of access and privileges assigned to a user or group on AI platforms.
- Sensitive AI Asset: Any data, model, or environment that, if compromised, poses significant risk to confidentiality, integrity, or availability.
- Authorized AI Agent: A validated AI system or subsystem identified within the Mind Matrix as having the authority to execute specific SDLC or operational tasks. |
7. Procedure Activities
7.1 Security Planning and Governance
- AI Project Sponsor consults with the AI IRB Liaison and Security Administrator to outline security needs for a new or ongoing AI project.
- Security Administrator reviews the initial scope from SOP-1000-01-AI and any relevant data compliance requirements (GDPR/HIPAA) to identify key security controls.
7.2 Access Control and Profile Management
- Security Administrator creates or updates Access Profiles that define roles, privileges, and resource restrictions within AI systems.
- Any request for user or service account creation follows:
- Requestor fills out a Security Change Request form detailing privileges needed.
- Security Administrator reviews the form against pre-approved profiles.
- If the requested privileges exceed standard profiles, the request is escalated to the AI IRB Liaison if there’s a potential ethical or regulatory concern.
- Temporary or situational access requires an expiration date or event-based lock-down.
7.3 Security Monitoring
- Security Administrator configures monitoring systems to track:
- System logs (authentication events, usage logs).
- Model inference logs for suspicious or out-of-pattern usage, including recursive self-improvement subroutine anomalies.
- Infrastructure logs from dev, QA, production AI environments.
- Security Administrator performs regular log reviews, focusing on unauthorized or unusual events.
7.4 Vulnerability and Patch Management
- Operations identifies OS or infrastructure patches relevant to AI systems.
- Security Administrator coordinates patching schedules with Operations and AI Development to ensure no disruption to critical AI workflows.
- Post-patch verification checks that all AI services remain operational with no new vulnerabilities.
7.5 Incident Response
- On detecting a security incident (intrusion, data breach, model tampering), Technical Support escalates to the Security Administrator.
- Security Administrator and AI IRB Liaison convene an incident team if the breach includes ethical or compliance ramifications.
- Triage the incident, gather logs, and isolate or disable compromised components.
- Implement remediation measures (e.g., revoke credentials, restore from backup, patch vulnerabilities).
- Document the incident, any lessons learned, and adjust relevant policies.
7.6 Periodic Security Audits
- Security Administrator arranges periodic internal or third-party audits of AI systems, verifying compliance with relevant regulations and policies, integrating Exochain Peer Reviews for automated compliance validation.
- Data Science / AI Engineering teams provide model details for algorithmic audits (fairness, data usage).
- Audit findings are reported to AI Project Sponsor, Program Manager, and the AI IRB if changes to risk posture are found.
- Security Administrator updates security policies or standard profiles based on the findings.
7.7 Post-Implementation Review
- After major AI projects or enhancements are deployed, a Post-Implementation Review is held by AI IRB Liaison, Security Administrator, and relevant stakeholders.
- They review any security incidents, near misses, and the overall success in maintaining compliance.
- Document best practices, new vulnerabilities discovered, and recommend improvements for future projects.
8. Metrics
- Number of Security Incidents: The total security incidents reported in a quarter.
- Mean Time to Respond (MTTR): Average time from incident detection to resolution.
- Access Control Exceptions: Count how many requests deviate from pre-approved access profiles.
- Patching Completion Rate: Percentage of identified critical patches applied by the recommended deadline.
9. Records and Documentation
| Record Name | Responsible | Storage Location | Retention |
|---|---|---|---|
| Security Change Request Forms | Security Administrator | Secure Shared Folder | 3 Years |
| Incident Reports | Security Administrator | GRC or security tool repository | 5 Years |
| Access Profiles Documentation | Security Administrator | Secure Shared Folder | Current Only |
| Audit Reports | Security Administrator | Secure Shared Folder | 5 Years |
| Post-Implementation Review Minutes | AI IRB Liaison | Project/IRB Document Repository | 5 Years |
10. Exemptions
- Sandbox or purely experimental AI prototypes that do not handle regulated data may be exempt from some procedures only if documented and approved by the AI IRB Liaison and the Security Administrator.
11. Tools/Software/Technology Used
- SIEM Solutions (Security Information and Event Management).
- Access Management (LDAP/Active Directory).
- Encryption Tools (e.g., at-rest and in-transit).
- Vulnerability Scanners (Nessus, OpenVAS).
- Patch Management (WSUS, or equivalents for Linux-based AI clusters).
- Ticketing (JIRA or ServiceNow) for tracking security requests and incidents.
12. Revision History
| Revision | Date | Author/Owner | Change Reference |
|---|---|---|---|
| 1.0 | YYYY-MM-DD | Security Administrator | Initial release |
END OF SOP
@startuml
title SOP-1050-01-AI (AI Security Administration and Governance)
actor “AI Project Sponsor” as SPS
actor “AI IRB Liaison” as AIL
actor “Security Administrator” as SAD
actor “AI Development Team” as ADT
actor “Data Science / AI Engineering Team” as DSE
actor “Operations” as OPS
actor “Quality Assurance” as QAS
actor “Technical Support” as TSP
’ 1. Security Planning and Governance
SPS -> AIL: Discuss AI project scope & compliance
AIL -> SAD: Provide IRB guidelines for new AI project
SAD -> SPS: Outline security control requirements
’ 2. Access Control and Profile Management
ADT -> SAD: Request new or modified access profiles
SAD -> SAD: Compare request to pre-approved profiles
alt Exceeds Standard Profile
SAD -> AIL: Escalate for IRB compliance check
AIL -> SAD: Approve or modify special access
else Within Standard Profile
SAD -> ADT: Grant access as requested
end
’ 3. Security Monitoring
SAD -> OPS: Configure log monitoring for AI environment
OPS -> SAD: Confirm monitoring is active
SAD -> SAD: Periodic log review for anomalies
’ 4. Vulnerability and Patch Management
OPS -> SAD: Identify critical patches
SAD -> ADT: Coordinate patch schedule
ADT -> OPS: Validate patch with no AI system disruption
OPS -> OPS: Deploy patches
OPS -> SAD: Patch completion status
’ 5. Incident Response
TSP -> SAD: Escalate suspected security incident
SAD -> AIL: Notify IRB if ethics/compliance impacted
SAD -> SAD: Investigate and isolate compromised resources
SAD -> DSE: Request logs/model checks if model compromised
SAD -> TSP: Provide resolution steps
SAD -> SAD: Document incident and lessons learned
’ 6. Periodic Security Audits
SAD -> QAS: Arrange internal/third-party security audits
QAS -> DSE: Request data usage & model details
QAS -> SAD: Report audit findings
SAD -> SPS: Summarize security posture & changes needed
’ 7. Post-Implementation Review
SAD -> AIL: Discuss final security outcomes & compliance
AIL -> SAD: Confirm no further IRB actions needed
SAD -> TSP: Communicate updated security guidelines
SPS -> All: Acknowledge final improvements
@enduml
Short textual explanation: This sequence diagram shows how AI security administration and governance steps are coordinated among various roles. The flow begins with planning and governance steps, continues through access control and monitoring, addresses patching and incident handling, includes periodic audits, and ends with a post-implementation review with IRB input.