Developing and managing AI is like assembling a cutting-edge machine from parts from around the world.
All components – models, vector databases, agents – come from different toolkits with their own specifications. Once everything is adjusted, new safety standards and compliance rules will require rewiring.
For data scientists and AI developers, this setup often feels confusing. Tracking issues, ensuring security, and compliance with regulatory standards across all generative and predictive AI assets requires constant vigilance.
This post outlines a practical AI governance framework and introduces three strategies to keep your projects secure, compliant, and scalable, no matter how complex they become.
Centralize oversight of AI governance and observability
Many AI teams say they struggle to manage their own tools, languages, and workflows while ensuring security across predictive and generative models.
Because AI assets are spread across open source models, proprietary services, and custom frameworks, maintaining control over observability and governance is often burdensome and difficult to manage.
We offer three new customization capabilities to help you unify oversight, centralize AI management, and build trusted operations at scale.
1. Bolt-on observability
Part of the Observability Platform, this feature enables comprehensive observability, intervention, and orchestration in just two lines of code to eliminate unwanted behavior across generative AI use cases built on Google Vertex, Databricks, Microsoft Azure, and open AI. It helps prevent this. Source tool.
It provides real-time monitoring, intervention and moderation capabilities and secures LLM, vector database, search augmented generation (RAG) flows and agent workflows to ensure project objectives and uninterrupted performance without additional tools or troubleshooting.
2. Advanced vector database management
New features allow you to maintain full visibility and control over your vector database, whether embedded in DataRobot or from another provider, ensuring a smooth RAG workflow.
Update vector database versions without disrupting deployments, while automatically tracking history and activity logs for complete oversight.
It also monitors key metadata, such as benchmarks and validation results, to identify performance trends, identify gaps, and support efficient and reliable RAG flows.
3. Code-first personalized refresher training
To simplify retraining, we’ve included customizable retraining strategies directly in your code, regardless of the language or environment used in your predictive AI model.
We design custom retraining scenarios, including feature engineering retooling and challenger testing, to achieve specific use case goals.
You can also configure triggers to automate retraining tasks, which can help you discover optimal strategies faster, deploy faster, and maintain model accuracy over time.
Embed compliance capabilities into every layer of generative AI.
Compliance for generative AI is complex, and each layer requires rigorous testing that few tools can address effectively.
Without strong, automated safeguards, you and your team risk unreliable results, wasted work, legal exposure, and potential harm to your organization.
To help you navigate this complex and changing environment, we’ve developed the industry’s first automated compliance testing and one-click documentation solution designed specifically for generative AI.
The EU AI Act, NYC Law No. Ensures compliance with evolving laws such as 144 and California AB-2013.
1. Automated red team testing for vulnerabilities
To help you identify the safest deployment options, we have developed rigorous testing for PII, rapid injection, toxicity, bias, and fairness to allow you to compare models side-by-side.
2. Create customizable, one-click AI compliance documents
Navigating the maze of new global AI regulations is never simple or fast. That’s why we’ve created reports you can use right away in one click to do the hard work.
By mapping key requirements directly to documents, these reports help you stay compliant, adapt to evolving standards, and free you from tedious manual reviews.
3. Production Guard Model and Compliance Monitoring
Our customers use comprehensive security systems to protect their AI systems. Now we’ve expanded to provide real-time compliance monitoring, alerts, and guardrails to keep your LLM and generative AI applications compliant and protect your brand.
One of the new additions to the arbitration library is PII masking technology to protect sensitive data.
Automated intervention and continuous monitoring help you immediately detect and mitigate unwanted behavior to minimize risk and secure your deployment.
By automating use-case-specific compliance checks, enforcing guardrails, and generating custom reports, you can develop with confidence that your models will remain compliant and secure.
Customize AI monitoring for real-time diagnostics and resiliency.
Monitoring is not one-size-fits-all. Each project requires custom boundaries and scenarios to maintain control over various tools, environments, and workflows. Delayed detection can lead to serious errors such as inaccurate LLM output or customer churn, while manual log tracking is slow and prone to missed alerts or false alarms.
Other tools make detection and remediation a complex and inefficient process. Our approach is different.
Known for our comprehensive, centralized monitoring suite, we enable full customization to meet your specific needs, ensuring operational resiliency for all generative and predictive AI use cases. Now we’ve enhanced this with deeper traceability with several new features.
1. Vector database monitoring and generative AI behavior tracking
You have complete oversight of the performance and troubleshooting of all your vector databases, whether embedded in DataRobot or from another provider.
Monitor prompts, vector database usage, and performance metrics during production to uncover undesirable results, low-reference documents, and gaps in document sets.
Track actions across prompts, responses, metrics, and assessment scores to quickly analyze and resolve issues, streamline your database, optimize RAG performance, and improve response quality.
2. Custom drift and geospatial monitoring
This allows you to customize predictive AI monitoring with target drift detection and geospatial tracking to suit your project requirements. Define specific drift criteria, monitor drift for any feature, including geospatial, and set alert or retrain policies to reduce manual intervention.
For geospatial applications, you can monitor location-based metrics such as drift, accuracy, and regional forecasts, drill down into underperforming geographic areas, and isolate them for targeted retraining.
Whether analyzing home prices or detecting anomalies like fraud, this feature accelerates time to insight and visually drills down and explores any geographic segment to maintain model accuracy across multiple locations.
Top performance starts with trustworthy AI
As AI becomes more complex and powerful, it becomes important to maintain both control and agility. Centralized oversight, regulatory readiness, and real-time intervention and coordination enable you and your team to develop and deliver AI that inspires confidence.
Adopting these strategies will provide a clear path to achieving resilient and inclusive AI governance, allowing you to innovate boldly and tackle complex challenges head-on.
To learn more about solutions for secure AI, check out our AI Governance page.
About the author
May Masoud is a data scientist, AI advocate, and thought leader trained in classical statistics and modern machine learning. At DataRobot, she designs market strategies for DataRobot AI governance products, helping global organizations achieve measurable returns on AI investments while maintaining corporate governance and ethics.
After developing her skill base with degrees in statistics and economics, May earned a master’s degree in business analytics from the Schulich School of Business. This combination of technical and business expertise has made May an AI practitioner and thought leader. May will feature Ethical AI and AI Democratization keynotes and workshops for the business and academic communities.
Meet May Massoud
