Developing and managing AI is like trying to assemble a high-tech machine from a global array of parts.
Every component—model, vector database, or agent—comes from a different toolkit, with its own specifications. Just when everything is aligned, new safety standards and compliance rules require rewiring.
For data scientists and AI developers, this setup often feels chaotic. It demands constant vigilance to track issues, ensure security, and adhere to regulatory standards across every generative and predictive AI asset.
In this post, we’ll outline a practical AI governance framework, showcasing three strategies to keep your projects secure, compliant, and scalable, no matter how complex they grow.
Centralize oversight of your AI governance and observability
Many AI teams have voiced their challenges with managing unique tools, languages, and workflows while also ensuring security across predictive and generative models.
With AI assets spread across open-source models, proprietary services, and custom frameworks, maintaining control over observability and governance often feels overwhelming and unmanageable.
To help you unify oversight, centralize the management of your AI, and build dependable operations at scale, we’re giving you three new customizable features:
1. Bolt-on observability
As part of the observability platform, this feature activates comprehensive observability, intervention, and moderation with just two lines of code, helping you prevent unwanted behaviors across generative AI use cases, including those built on Google Vertex, Databricks, Microsoft Azure, and open-sourced tools.
It provides real-time monitoring, intervention and moderation, and guards for LLMs, vector databases, retrieval-augmented generation (RAG) flows, and agentic workflows, ensuring alignment with project goals and uninterrupted performance without extra tools or troubleshooting.
2. Advanced vector database management
With new functionality, you can maintain full visibility and control over your vector databases, whether built in DataRobot or from other providers, ensuring smooth RAG workflows.
Update vector database versions without disrupting deployments, while automatically tracking history and activity logs for complete oversight.
In addition, key metadata like benchmarks and validation results are monitored to reveal performance trends, identify gaps, and support efficient, reliable RAG flows.
3. Code-first custom retraining
To make retraining simple, we’ve embedded customizable retraining strategies directly into your code, regardless of the language or environment used for your predictive AI models.
Design tailored retraining scenarios, including as feature engineering re-tuning and challenger testing, to meet your specific use case goals.
You can also configure triggers to automate retraining jobs, helping you to discover optimal strategies more quickly, deploy faster, and maintain model accuracy over time.
Embed compliance into every layer of your generative AI
Compliance in generative AI is complex, with each layer requiring rigorous testing that few tools can effectively address.
Without robust, automated safeguards, you and your teams risk unreliable outcomes, wasted work, legal exposure, and potential harm to your organization.
To help you navigate this complicated, shifting landscape, we’ve developed the industry’s first automated compliance testing and one-click documentation solution, designed specifically for generative AI.
It ensures compliance with evolving laws like the EU AI Act, NYC Law No. 144, and California AB-2013 through three key features:
1. Automated red-team testing for vulnerabilities
To help you identify the most secure deployment option, we’ve developed rigorous tests for PII, prompt injection, toxicity, bias, and fairness, enabling side-by-side model comparisons.
2. Customizable, one-click generative AI compliance documentation
Navigating the maze of new global AI regulations is anything but simple or quick. This is why we created one-click, out-of-the-box reports to do the heavy lifting.
By mapping key requirements directly to your documentation, these reports keep you compliant, adaptable to evolving standards, and freedom from tedious manual reviews.
3. Production guard models and compliance monitoring
Our customers rely on our comprehensive system of guards to protect their AI systems. Now, we’ve expanded it to provide real-time compliance monitoring, alerts, and guardrails to keep your LLMs and generative AI applications compliant and safeguard your brand.
One new addition to our moderation library is a PII masking technique to protect sensitive data.
With automated intervention and continuous monitoring, you can detect and mitigate unwanted behaviors instantly, minimizing risks and safeguarding deployments.
By automating use case-specific compliance checks, enforcing guardrails, and generating custom reports, you can develop with confidence, knowing your models stay compliant and secure.
Tailor AI monitoring for real-time diagnostics and resilience
Monitoring isn’t one-size-fits-all; each project needs custom boundaries and scenarios to maintain control over different tools, environments, and workflows. Delayed detection can lead to critical failures like inaccurate LLM outputs or lost customers, while manual log tracing is slow and prone to missed alerts or false alarms.
Other tools make detection and remediation a tangled, inefficient process. Our approach is different.
Known for our comprehensive, centralized monitoring suite, we enable full customization to meet your specific needs, ensuring operational resilience across all generative and predictive AI use cases. Now, we’ve enhanced this with deeper traceability through several new features.
1. Vector database monitoring and generative AI action tracing
Gain full oversight of performance and issue resolution across all your vector databases, whether built in DataRobot or from other providers.
Monitor prompts, vector database usage, and performance metrics in production to spot undesirable outcomes, low-reference documents, and gaps in document sets.
Trace actions across prompts, responses, metrics, and evaluation scores to quickly analyze and resolve issues, streamline databases, optimize RAG performance, and improve response quality.
2. Custom drift and geospatial monitoring
This enables you to customize predictive AI monitoring with targeted drift detection and geospatial tracking, tailored to your project’s needs. Define specific drift criteria, monitor drift for any feature—including geospatial—and set alerts or retraining policies to cut down on manual intervention.
For geospatial applications, you can monitor location-based metrics like drift, accuracy, and predictions by region, drill down into underperforming geographic areas, and isolate them for targeted retraining.
Whether you’re analyzing housing prices or detecting anomalies like fraud, this feature shortens time to insights, and ensures your models stay accurate across locations by visually drilling down and exploring any geographic segment.
Peak performance starts with AI that you can trust
As AI becomes more complex and powerful, maintaining both control and agility is vital. With centralized oversight, regulation-readiness, and real-time intervention and moderation, you and your team can develop and deliver AI that inspires confidence.
Adopting these strategies will provide a clear pathway to achieving resilient, comprehensive AI governance, empowering you to innovate boldly and tackle complex challenges head-on.
To learn more about our solutions for secure AI, check out our AI Governance page.
The post New AI governance solutions for trust, security, and compliance appeared first on DataRobot.
