AI can bring a whole host of efficiencies to your API management processes. It can also raise questions around governance, bringing with it new challenges as well as new opportunities. That’s where this article comes in. We’ll walk you through what AI governance is and why you need to care about it if you want to get the best from your APIs.
What is AI governance?
AI governance can mean a couple of different things. At the macro level, it refers to the frameworks and guardrails that support the safe, ethical use of AI systems and tools. AI governance ensures responsible use of AI, where users are accountable, and AI models are transparent and bias-free.
In an API management context, AI governance can refer to the incorporation of AI into the processes used to govern API ecosystems. This includes using AI to enhance API lifecycle management, security, scalability, compliance, troubleshooting and more. AI can introduce efficiencies and automations at numerous points, supporting you to get more from your APIs.
Why is AI governance important?
As we mentioned above, AI has huge potential when it comes to delivering efficiencies and automations within your API management and governance processes. Combined with the fact that APIs have become central to digital transformation around the globe, this means AI is increasingly important.
Also relevant is the growing intersection of AI and API management. Organizations around the world are incorporating AI into their workflows, using it for everything from streamlining processes and automating risk management to establishing predictive analysis systems. The result is that robust AI governance is now essential if organizations are to meet their obligations in terms of data security, privacy and regulatory compliance.
Can you ignore the need for AI governance?
Not really, no. Tyk’s 2024 API platform insights research found that 26% of organizations are already using AI-powered tools. Meanwhile, 24% of the businesses we spoke to were already exploring AI solutions, with plans to start using them later in 2024. A further 24% has AI-powered tools scheduled for adoption in 2025/26. Only 22% of respondents said that they were yet to use AI-powered tools.
Such widespread and growing usage highlights the need for AI governance frameworks to be in place now, not at some vague future point.
The benefits of AI governance
Implementing AI governance can deliver plenty of rewards.
Efficiency and automation
You can use AI to automate everything from the detection of potential API security breaches to their mitigation. AI-driven automation can also identify API failures and compliance risks faster, while reducing the need for manual oversight.
Predictive analysis
AI can’t tell you for sure what the future will hold, but it can certainly predict it. You can use predictive analysis to forecast usage trends, helping you get in front of traffic spikes and load issues before they become a problem.
Proactive adjustments
AI can automatically make adjustments in response to real-time API usage patterns, adapting governance parameters to head off potential issues.
Let’s say, for example, that your AI system detects a high frequency of requests from certain IP addresses. It could identify this as a potential distributed denial of service (DDoS) attack and dynamically adjust throttling, rate limiting and access control in response to it, at the same time as alerting the relevant people within your business.
The rapid reaction can assure API performance and data integrity during the DDoS attack, getting ahead of the problem faster than manual identification and intervention could.
Enhanced compliance monitoring
You can use AI to assist with compliance monitoring, ensuring you continue to meet your obligations in respect of global standards and industry regulations.
Improved decision-making
AI’s data analysis power is well established. You can put this to use as part of your governance, underpinning improved decision-making based on clearly insights into API usage, consumer behavior, performance metrics and more.
Superior scalability
With AI helping optimize API calls, through smart caching, predictive traffic management, response pre-fetching and more, you can iron out inefficiencies and lay the foundations for superior scalability.
AI governance challenges
As with any new technology, there are challenges and pitfalls to consider when it comes to AI governance. One pitfall to avoid is the use of opaque AI systems, where you can’t see or understand the reasoning behind AI decisions. These can add unnecessary complications. As we mentioned above, transparency is important when it comes to AI governance. Transparency can help when it comes to regulatory compliance, too.
Another mistake to avoid is an over-reliance on AI-drive automation. Yes, AI can bring plenty of time-saving automations to your API governance systems. However, that should not be at the expense of human oversight. Sometimes, decisions call for a degree of nuance and an understanding of context that AI may not be able to deliver.
It’s also important to watch out for the perpetuation of bias in AI systems. Whether you’re analyzing API traffic or powering your decision-making with AI-driven insights, bias can skew the results. Careful management is an important means of avoiding this.
Proper governance of your AI systems is also essential from a data security and privacy perspective. You’ll need to maintain control over the decisions your AI is making, to avoid any unintentional data leaks. You’ll also need to protect your AI systems from attack with robust governance and security, just as you do with your APIs.
AI and accountability
No discussion of AI governance is complete without considering accountability. It’s something that regulators and governments are focusing on, so it needs to be on your organizational radar too. Part of that accountability involves implementing a robust governance framework for your API management to help you adhere to your compliance obligations.
You’ll also need to keep abreast of emerging legislation. The European AI Act came into force on 1st August 2024. It was the world’s first legal framework on AI but is unlikely to be the last. Evolving your AI governance framework in line with emerging standards is therefore something that every business will need to embrace.
Of course, AI accountability isn’t just about keeping regulators and law enforcers happy. Transparent AI governance is also important for establishing and maintaining consumer trust. Views on the use of AI vary considerably and not all consumers will appreciate AI making decisions on matters such as throttling or rate limiting. As such, it’s important to be clear on how and why you use AI in your API management systems, and to reassure consumers regarding your robust AI governance principles.
Conclusion – why you should care about AI governance
AI governance is crucial to getting the best out of your API management processes while also keeping regulators and consumers happy. AI can support you to achieve a more efficient, optimized, scalable infrastructure. It also introduces unique challenges, as we’ve discussed above, meaning a proactive approach to AI governance is essential.
Ready to discover more? We’ll be discussing API governance in huge depth at Tyk’s online LEAP 2.0 API Governance Conference on 25th February 2025. We’ll dive into topics relating to AI governance, as well as industry best practices, API security, observability, automation and federated API management. Why not register now to join us?
