OpenDerisk is an open-source framework enabling developers to comprehensively assess AI model risks. It supports automated evaluations of fairness, privacy, robustness, security, and compliance through built-in probes and custom modules. Users integrate OpenDerisk into CI/CD pipelines or run standalone risk scans across datasets and model outputs. The platform generates detailed reports and recommendations, helping teams identify vulnerabilities and mitigate potential issues early in development.
AI Platforms & Frameworks
Observability & Monitoring
Security
#AI risk assessment
#fairness metrics
#privacy protection
#adversarial testing
#Bias Detection
#data leakage prevention
#Risk Management Tools
#AI Compliance
#automated reporting
#Python SDK
#ci/cd integration
#Open-source AI
#quality assurance
#risk mitigation
#interactive analytics
#custom modules
#vulnerability management
#ethical AI
#Continuous Integration
#AI safety
...
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OpenDerisk
OpenDerisk is an open-source framework enabling developers to comprehensively assess AI model risks. It supports automated evaluations of fairness, privacy, robustness, security, and compliance through built-in probes and custom modules. Users integrate OpenDerisk into CI/CD pipelines or run standalone risk scans across datasets and model outputs. The platform generates detailed reports and recommendations, helping teams identify vulnerabilities and mitigate potential issues early in development.
AI Platforms & Frameworks
Observability & Monitoring
Security
#AI risk assessment
#fairness metrics
#privacy protection
#adversarial testing
#Bias Detection
#data leakage prevention
#Risk Management Tools
#AI Compliance
#automated reporting
#Python SDK
#ci/cd integration
#Open-source AI
#quality assurance
#risk mitigation
#interactive analytics
#custom modules
#vulnerability management
#ethical AI
#Continuous Integration
#AI safety
...
Featured
What is OpenDerisk?
OpenDerisk provides a modular, extensible platform to evaluate and mitigate risks in AI systems. It includes fairness evaluation metrics, privacy leakage detection, adversarial robustness tests, bias monitoring, and output quality checks. Users can configure pre-built probes or develop custom modules to target specific risk domains. Results are aggregated into interactive reports that highlight vulnerabilities and suggest remediation steps. OpenDerisk runs as a CLI and Python SDK, allowing seamless integration into development workflows, continuous integration pipelines, and automated quality gates to ensure safe, reliable AI deployments.
Who will use OpenDerisk?
- Machine learning engineers
- Data scientists
- AI researchers
- Compliance officers
- Quality assurance teams
- AI auditors
How to use the OpenDerisk?
- Step1: Clone the OpenDerisk repository from GitHub.
- Step2: Install Python and required dependencies via pip.
- Step3: Configure risk modules and specify data or model endpoints.
- Step4: Execute risk assessments using the CLI or Python SDK.
- Step5: Review and export detailed risk reports and visualizations.
- Step6: Integrate OpenDerisk into your CI/CD pipeline for continuous monitoring.
Platform
- mac
- windows
- linux
OpenDerisk's Core Features & Benefits
The Core Features
- Fairness evaluation metrics
- Privacy leakage detection
- Adversarial robustness testing
- Bias and toxicity monitoring
- Custom risk pipeline modules
- Detailed interactive reporting
- CLI and Python SDK interfaces
- CI/CD integration support
The Benefits
- Early identification of AI model vulnerabilities
- Compliance and regulatory alignment
- Open-source extensibility and customization
- Automated risk scans in development workflows
- Actionable insights and remediation recommendations
OpenDerisk's Main Use Cases & Applications
- Pre-deployment AI model governance
- Continuous risk monitoring in CI/CD
- Compliance auditing for fairness and privacy
- AI safety validation and robustness checks
- Research on bias mitigation techniques
FAQs of OpenDerisk
OpenDerisk Company Information
- Derisk AI
OpenDerisk Reviews
5/5
OpenDerisk's Main Competitors and alternatives?
- IBM AI Fairness 360
- Microsoft Fairlearn
- IBM Adversarial Robustness Toolbox (ART)
- Google What-If Tool
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