SentinelMesh is an AI governance specification designed to provide intelligent oversight for artificial systems through risk assessment, safety evaluation, transparency, monitoring, and federated governance. It establishes a modular framework for understanding AI system behavior, evaluating potential risks, and supporting accountable deployment throughout the AI lifecycle.
SentinelMesh enables organizations to analyze how dangerous an AI system may become, measure its broader impact, evaluate safety boundaries, and maintain continuous governance through transparent and distributed oversight mechanisms.
Provides foundational records for AI systems, including:
- AI system registration
- Model identity records
- Version tracking
- Ownership records
- Deployment inventory
- Dependency mapping
Evaluates and analyzes AI system risks through:
- Risk assessment engines
- Threat modeling
- Harm analysis
- Scenario simulation
- Risk forecasting
- Risk scoring models
Evaluates the broader effects of artificial systems, including:
- Social impact analysis
- Economic impact analysis
- Environmental impact analysis
- Accessibility impact analysis
- Workforce impact analysis
- Community impact analysis
Provides structured testing and validation methods:
- Capability testing
- Failure testing
- Adversarial testing
- Red team evaluation
- Boundary testing
- Stress testing
Creates visibility into AI systems through:
- Decision records
- Model documentation
- System documentation
- Explainability reports
- Data lineage tracking
- Audit documentation
Supports governance throughout the lifecycle:
- Design review
- Development approval
- Deployment controls
- Change management
- Version governance
- Retirement management
Provides continuous observation of AI behavior:
- Behavior monitoring
- Output analysis
- Drift detection
- Performance monitoring
- Compliance tracking
- Incident detection
Addresses security risks associated with artificial systems:
- Prompt injection detection
- Model attack detection
- Supply chain security
- Access control analysis
- Credential monitoring
- Vulnerability tracking
Ensures accountable human involvement:
- Human approval workflows
- Escalation procedures
- Human override controls
- Review queues
- Accountability assignment
Supports governance requirements through:
- Policy rules
- Policy enforcement
- Regulatory mapping
- Compliance evidence
- Audit preparation
Manages AI system failures and unexpected behavior:
- Incident classification
- Automated alerts
- Containment actions
- Root cause analysis
- Corrective actions
- Recovery tracking
Provides emergency controls for high-risk situations:
- Emergency shutdown
- Capability restriction
- Isolation mode
- Rollback procedures
- Human takeover controls
- Crisis response workflows
Enables distributed governance and shared intelligence:
- Shared risk intelligence
- Governance exchange
- Community reporting
- Reputation tracking
- Distributed oversight
- Federated governance networks
Supports deployment across different environments:
- AI platforms
- Enterprise systems
- Local deployments
- Cloud environments
- Open source AI ecosystems
SentinelMesh is built around:
- Modular architecture
- Vendor-neutral governance
- Local-first deployment
- Human-in-the-loop oversight
- Transparent risk evaluation
- Auditable decision processes
- Federated knowledge sharing
- Responsible AI development
SentinelMesh follows a continuous governance process:
- Register the AI system
- Assess system risks
- Evaluate potential impacts
- Test safety boundaries
- Establish governance controls
- Monitor system behavior
- Maintain transparency records
- Respond to incidents
- Share governance intelligence
SentinelMesh can support governance for:
- Enterprise AI systems
- Autonomous AI agents
- Research systems
- Government AI deployments
- Healthcare AI applications
- Financial AI systems
- Educational AI platforms
- Robotics and autonomous systems
SentinelMesh provides a foundation for organizations to understand, evaluate, and govern artificial systems through shared standards, transparent processes, and continuous oversight.
The goal is to create a scalable governance framework where AI systems can be monitored responsibly while enabling innovation and collaboration.
- Fully AGPL-3.0+ compliant system
- Copyleft enforced for network deployments
- Required attribution:
- Roxanne Ardary
- https://www.roxanneardary.com/
- Specification Branding License (SBL)
- Attribution-free commercial deployment
- Pricing based on scale, usage, and deployment scope
- https://roxanneardary.com/sentinelmesh/
SentinelMesh is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).
By contributing to any Open Arsenal project, you agree that your contributions will also be released under this license.
Please note the following:
- All contributions must comply with the AGPL-3.0+ terms.
- Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to:
Roxanne Ardary and roxanneardary.com. - SentinelMesh specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
- The project's notice.md file tracks attribution requirements and contributor acknowledgments.
Any update that adds new contributors or modifies attribution should also updatenotice.md. - When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
- Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.
For full legal details, please refer to the AGPL-3.0+ license and the project's notice.md file.