AI can strengthen API governance – but AI itself needs to be governed when it’s used within APIs. These two different aspects of AI and governance in the API ecosystem were put under the spotlight by Matt Tanner, Head of Growth Engineering at SingleStore, at the recent LEAP 2.0 API governance conference. Examining the topic from all angles, Matt covered:
- Why AI governance matters
- The key challenges of the intersection of AI and API governance
- Best practices for applying API
- Tools and techniques to for building AI-drive API governance
Why AI governance matters for API professionals
We’ve seen a rapid expansion of API usage in recent years, as APIs have proliferated across multiple teams and products. And, as we get more APIs out there, governance complexity scales with each new API endpoint.
Most of us are also now using AI, whether to build APIs or within APIs themselves. Adding AI into the mix introduces additional data processing and decision layers, so you need proper oversight to prevent misconfigurations, security gaps and so on.
Ramping the complexity up further is the growing use of AI in the APIs themselves, amplifying the need for more robust governance as AI logic becomes embedded in API workflows. AI endpoints enable dynamic decision making and personalization, which is awesome – as we’re seeing with many of the new apps coming through this AI wave. However, it also leads to a bias and data quality issue that can creep up, becoming even more pronounced when there isn’t strong governance in place. This means the regulatory spotlight intensifies on AI driven endpoints as adoption grows.
Using AI also comes with elevated risks and complexity, such as model drift, AI decision-making transparency and frequent updates that add to the complications of API versioning.
On top of all of this, using AI means there’s a wider threat surface for security breaches and data leaks. This means compliance requirements need to grow alongside adoption.
Key challenges of AI and API governance
Let’s dive deeper into the challenges we’ve mentioned above.
- Data quality and bias: Poor or unrepresentative data fueling AI models served by APIs can produce skewed, unfair and unreliable AI outputs.
- Model versioning and lifecycle: Misaligned updates can break API consumers, with updates conflicting with standard API versioning practices and creating confusion and potential rollback issues.
- Explainability and transparency: Traditional APIs have well-defined input/output specs, but AI-driven APIs can be black boxes that lack clear and transparent decision paths.
- Security and privacy: Sensitive data handling requires airtight controls. If you have an AI component or service that your API is leveraging, and it has access to proprietary or highly sensitive data, how do you prevent it from accessing that or sending it back to an API consumer?
- Regulatory compliance: AI-enabled APIs must adhere to emerging AI regulations, as well as existing data protection, privacy, financial, healthcare and other sector-specific rules.
Best practices for AI usage and applying AI to governance initiatives
The first essential best practice is to establish a model registry. Through this you can track model versions, training data sources, performance metrics and deployment endpoints. All crucial knowledge.
It’s also important to try and embed AI governance into the API lifecycle. Adding AI components to the traditional design/build/test lifecycle means focusing on their impact and how to minimize any related disruptions.
Prioritizing governance using a risk-based approach is another best practice. Some APIs that are leveraging AI may not have access to sensitive data, while others do. Prioritizing governance resources on higher-impact APIs is therefore a helpful approach.
Then it’s time to think about cross functional collaboration – about ensuring your legal, compliance and product teams are all in the loop when it comes to how you’re governing the AI portion of your APIs.
Finally, continuous monitoring and feedback loops are essential. AI models degrade over time, so you’ll need to tie routine audits and retraining processes into your API governance cycles.
AI-driven API governance tools and techniques
Many API gateways already have AI plugins available. These can help with a wide range of tasks, from threat detection and traffic shaping to adaptive rate limiting. By embracing them, you can add an automated layer of governance into your APIs that enhances both security and performance.
There’s also AI-integrated DevSecOps, which enables you to leverage AI for automated vulnerability scanning and compliance checks. Using companies and tools such as StackHawk, GitHub Advanced Security and JFrog, you can build AI-integrated security governance into your CI/CD pipeline.
Many observability and telemetry tools also integrate AI. Moesif, Splunk and Dynatrace, for example, employ AI-based anomaly detection and correlation. You can use them to help pinpoint anomalies and errors and maintain governance visibility.
The final technique to consider is the use of policy as code, where you can use Open Policy Agent and automated logic to enforce policies for consistent governance across your entire infrastructure.
Why not explore the topic of policy as code right now, and see how it is empowering enterprise teams to succeed?
