API Security: Best Practices to Mitigate Risks in the AI Landscape

Overview

• APIs are fundamental linking elements within the software development realm.
• The rise of genAI and new architectures has made API Security a top-priority concern.
• Some best practices and a holistic API strategy could help mitigate emerging risks.

Nowadays, organizations are increasingly relying on Application Programming Interfaces (APIs) to facilitate the digitalization of products and provide modern, innovative digital solutions and experiences.

In a way, APIs act as the connective tissue between proprietary assets and third party data, simplifying interoperability and services’ integration, and fueling efficient application functionalities. 

However, with great linking functionality comes a great security risk. Since APIs are literally a means to connect data, applications and services, API Security has progressively become a top-priority concern when it comes to software engineering. 

Think of APIs as the drawbridge of a huge ancient castle. Fortresses were susceptible to be sieged and breached, and hordes of enemies could pass through the bridge if it was weak and unsecured, thereby exposing hidden treasures to easy loot.  

Similarly, APIs’ exposure is straightforward, but effectively defending them is a little bit more complex. This difficulty is even complicated by their widespread use and the frequent lack of organizational awareness regarding their own API landscape, resulting in inadequate security measures.

To make matters worse, APIs are now widely used by generative AI (GenAI) for data access to provide model-training data, leading to an increased need to protect that access to data.

The risk of massive attack surfaces to APIs has exponentially increased and an unmanaged API growth is likely to surpass the capabilities of API management tools, leading to difficulties in defending against emerging threats. Therefore, it is advisable to build a mature API strategy to enhance API security and guarantee the protection of underlying data and services they expose. 

This article will explore some best practices to adopt in relation to API security when developing applications in the revolutionizing era of AI.

API Security: What Does It Mean?

Considerable effort of many organizations in digitalizing their products often relies upon the foundational role of APIs.

In the ever-growing landscape of application development, these gluing elements are proliferating and are systematically called to orchestrate information flows that are, however, increasingly becoming more complex to handle.

API Security refers to the set of methodologies, best practices, and technologies that are designed to protect APIs from violation attempts, or prevent information from being accessed and stolen in case of system attacks.

Assuring the security of APIs is not something that can be taken lightly: a secure API management should be indeed a crucial part of application design from the very beginning, with several teams involved in the process.

However, it’s not always simple to guarantee a coherent security plan. APIs weave the threads of so many different services that it’s necessary to think on a case-by-case basis.

One thing is certain: if API security was already a significant concern, the rise of composable architectures and genAI has made it a top priority in software development.

API Security: How It Changed Over Time

In the past, APIs were important yet much simpler to handle. Single, monolithic systems required limited communication patterns confined to the same framework. But new architectural patterns based on cloud-native technologies gradually superseded monolithic systems.

Microservices first and Packaged Business Capabilities later, have eventually become essential elements to fuel composable architectures and enable composable businesses. 

Think of them as distinct cells: PBCs are the “cellular” building blocks of a digital organization, each performing a specialized function and working together to create a larger, functioning system. 

Within this architecture, API endpoints are crucial for system communication. Similar to cellular signaling within an organism, where interconnected actions lead to overall function, PBCs rely on APIs to interact and support integrated workflows.

Then, genAI joined the party and further expanded API’s range, demanding numerous high quality, secure APIs to seamlessly and effectively communicate between different services.

With thousands of applications, devices and systems to be connected, the need for providing massive amounts of relevant contextual data has surged, triggering new security challenges related to code quality, attack surfaces and to the potential exposure of private data.

That’s why organizations should adopt a meticulous approach featuring a full lifecycle management of APIs and implement a strategic AI-ready API management plan across all the layers of the organization.

API Security: Why It Matters With AI

The increasing number of AI initiatives saw a major shift in focus on security in software development and, in turn, on API security. 

According to Gartner, by 2028, API management will be a foundational module of a business’ AI application architecture. Concurrently, by the same year, more than half of all API security incidents will originate from vulnerabilities associated with AI.

Arguably, a mature API strategy means a more reasonable AI successful implementation. 

As a matter of fact, when integrating AI into software, its hunger for data is fed through APIs. This growing reliance introduces significant risks, as AI agents and large language models (LLMs) create new pathways for potential data breaches and, as a result, unforeseen costs. 

Without a strong API security strategy, organizations can face exposed private data, uncontrolled spending, and unmanaged access. Therefore, securing these API connections is a critical first step, ensuring that AI is implemented safely and effectively by managing, securing, and protecting the flow of information.

API Security: What Are The Challenges of a Robust Strategy?

Over the past years, most security threats have come from misconfigured and/or outdated APIs, which end up being put aside but are still a menace for data exposure.

The implications are many, one of them being hindered or delayed innovations due to security concerns. 

Apart from that, an additional pain point involves a misalignment between API development and core business objectives which leads to underperforming initiatives. Without a clear view of how APIs drive business goals, and a commitment to continuously monitoring and evolving the strategy, these programs can lose their impact and adaptability over time. 

For this reason, it’s crucial for IT leaders to regularly assess their API maturity, align their technical roadmaps with business outcomes, and ensure their API strategy remains a dynamic tool for growth.

Broadly speaking, assessing a mature and solid strategy for API Security could imply several challenges:

• API Visibility: The proliferation of APIs is directly proportional to the difficulty in tracking them. Without a centralizing tool that provides easy discoverability features, it’s like searching for a needle in a haystack. 
• Third-Party APIs: While third-party APIs offer higher flexibility and diversity, they are undoubtedly difficult to handle at scale and could put at risk the entire ecosystem.
• API Adaptability: APIs are adaptable and easily updatable. If on the one hand this ensures streamlined workflows and business agility, on the other hand they are likely to put into play new vulnerabilities that constantly require specific measures.
• New Architectures: APIs are increasingly leveraging new application architectures, frameworks, and languages, boosting their traffic as never before. This relentless growth is outpacing the ability of security teams to manage them effectively. 
• Lack of Skills: Recent surveys prove a widespread developers’ lack of knowledge and required experience in terms of both application and API security. 
• Compliance & Governance: Without a standardizing tool to manage APIs’ compliance and governance, developers could lack consistency in delivering services and find a hard time implementing authentication and authorization policies.

How to tackle all these challenges? Let’s find out together.

API Security: Why a Comprehensive API Management Matters

API’s widespread connectivity has led to uncontrolled data sharing.

Since APIs happen to bundle data, services, and business logic they are often designed and used as products, in that they have a specific value proposition and could be even monetized. 

Basically, they are very enablers of composable enterprises. A full lifecycle API management allows an easy management and governance of APIs at every stage of the software lifecycle.

This doesn’t deal only with reducing the time to market and lowering costs, but also with strengthening API security by viewing it as a critical business asset, ensuring the integrity of its unique functions and services, besides the more technical aspects.

But what does it mean in practice? A holistic API management includes gateways, catalogs, and developer portals, among other things. It involves designing infrastructural and architectural choices that help API be secure throughout its lifespan. For example:

• Defining specifications and schemas for clear communication.
• Registering APIs in a catalog, classified by criticality and data sensitivity.
• Maintaining visibility continuously monitored.
• Implementing well-defined versioning and deprecation strategies.
• Automating API governance.

After acknowledging API Security is just one piece of a multifaceted composition that requires deep understanding and awareness of a long term maturity strategy, we’ll now exemplify some of the best practices, tools, and technologies that may help fulfil this approach.

API Security: Best Practices, Tools, and Technologies

API Security policies should be addressed from the initial phases of the software design with Secure-by-design practices. These policies may concern system infrastructure or affect the design of components and their relationships – that’s to say architectural strategies.

Moreover, because APIs are usually operated by different teams on different infrastructures, it is advisable to develop neutral API security policies that integrate into and align with pre-existing, broader policies. This is achievable through composable modules that regulate, for example, access control, traffic throttling, key authentication, or transport security.  

API Security: Infrastructure Strategies

• Infrastructure Automation: Leveraging an Internal Developer Platform (IDP) could enforce the automation of API security and compliance. The platform is designed as a single ecosystem that makes use of principles like Infrastructure as Code (IaC) and Policy as Code (PaC) to enhance standardization and consistency across environments.
• Comprehensive Testing: Implementing Application Security Testing (AST) tools could help identify real-time risk and secure coding. Other practices include the integration of security and quality controls into CI/CD pipelines, using Dynamic Application Security Testing (DAST) to detect vulnerabilities and anomalies, and Consumer-Driven Contract Testing (CDCT) to validate API usage or prevent breaking changes.
• Paved Roads: An Internal Developer Portal can help developers produce secure software more easily and enable consistent security and governance. Platform teams can help reduce risk exposure by building nonfunctional requirements, such as security, into paved roads or standardized pathways for developers. 
• Connection Encryption: Implementing an HTTPS protocol (Hypertext Transfer Protocol Secure) assures the encryption of communication channels during data transmission, by using a cryptographic network protocol called TLS (Transport Layer Security). The HTTPS protocol provides the website with a digital certificate that proves its authenticity. To further increase security levels in B2B contexts, it is also possible to implement a Mutual TLS (mTLS) technique, which can securely identify the client who connects to the server.
• API Throttling: The management and limitation of API requests is pivotal, because setting limits and quotas for the total number of API requests prevents systems from exceeding the allowed request rate. In other words, this is a proactive measure against DoS (Denial of Service) or DDoS (Distributed DoS) attacks.
• VPN Networks. Securing network connections, segregation, and isolation mechanisms at the infrastructure level is also very important. Virtual Private Network (VPN) solutions help establish secure connections between local networks and client devices, preventing information from undergoing man-in-the-middle attacks.

API Security: Architectural Choices & Tools

• API Gateway: API Gateways are essential architectural components for controlling access, managing traffic, and enforcing security policies. They decouple API consumers from service implementations, integrate with identity infrastructure, protect against attacks, and can be part of a multilayered security model.
• Specialized Security Tools: API Gateways are insufficient alone for comprehensive API security. While gateways enforce policies like access, traffic, and request management, specialized API security tools are necessary for enhanced visibility and threat protection. These tools can detect complex issues like business logic vulnerabilities and threats that gateways might miss. Examples include Web Application Firewalls (WAFs), bot detection, and DDoS protection.
• Effective Access Control: Implementing strong access control mechanisms (authentication and authorization) ensures only authorized entities can access sensitive data and systems. Here are a few examples:

• • OAuth2: A token-based authentication standard that enables authorization by allowing other system components to verify access based on claims and scopes included in the tokens. This way, users don’t need to directly share their access credentials. 
  • JSON Web Tokens: JWT is a specific token format that holds and carries information (claims) for authentication. It’s like the entry ticket to be used within the broader permission framework (OAuth 2.0).
  • RBAC & ABAC: Role-Based Access Control and Attribute-Based Access Control are mechanisms used to define permissions. RBAC organizes permissions hierarchically and scales well, focusing on roles. In contrast, ABAC provides dynamic, context-aware permissions based on attributes.
  • Access Control List: Users and applications can be put into a static list (ACL), which defines interrelations (access and interaction) between users and specific resources.

• Avoid Anti-Pattern: Specific anti-patterns in related architectural areas like microservices can negatively impact security or desired outcomes. Some of these are, for example, confusing APIs with their implementation architecture, or providing APIs that lack specifications, versions, and testing.

Let’s shift gears. After a thorough breakdown of recommendations, it’s crucial to examine how AI alters API security dynamics.

API Security & AI: Supplementing Strategies

The sprawling of GenAI has directly impacted the Software Development Lifecycle (SDLC) and, consequently, all the security discourse revolving around API.

The difficulty lies in adapting one’s own long term strategy so that it fits a dynamic state where AI agents and genAI tools make tons of new APIs that spread like wildfire and end up being uncontrollable, or they don’t adhere to the same standards of APIs created by developers.

Ensuring an effective API lifecycle management in an AI-driven setting requires that APIs generated by AI programs are tracked and managed with the same level of rigor applied to those developed by human engineers. 

Then, organizations should provide a thorough documentation of their API standards and make it available to feed AI models for contextualized outcomes. 

Strict versioning and governance policies are essential for predictive reasons to mitigate the risks associated with vulnerabilities and unexpected changes due to rapidly evolving AI models. 

Finally, automated testing tools could help validate all APIs and assure quality is always preserved.

When AI enters the game, a solid API security strategy requires a better consolidation of existing practices. A few examples are:

• Access management should be tailored to AI agents, by assigning them unique identities and permissions rather than using shared developer credentials.
• Authentication and authorization policies should be modeled on a case-by-case scenario to prevent unwanted data exposure and to confirm that AI models generating APIs are data-privacy aware.
• Continuous collaboration with data and security engineers is beneficial to ensure underlying assets are approved for AI consumption.
• Operational data should undergo active monitoring. This can be achieved by utilizing API security tools equipped with integrated AI for identifying threats proactively.
• Throttling policies should be refined for predictive efficiency to manage traffic surges potentially generated by AI applications.

How to enforce API policies? How to claim full control over APIs and ensure they become valuable assets for your organization’s growth? 

The answer might lie in composable AI-powered platforms. Mia-Platform, for instance, is an AI-Native Developer Platform Foundation that offers one intelligent centralizing tool for full API governance, granting the standardization of delivery processes and security policies.

Wrapping Up

The current software development landscape is dominated by APIs, groups of protocols, rules, and definitions that enable the connection and communication between different applications.

Since these key elements allow massive sharing of data, services, and business logic, a consolidated strategy in terms of API security is fundamental to prevent leaks and breaches.  

However, new architectures based on cloud-native technologies and the disruptive generative AI have led to major security issues in application development and, in turn, raised newer concerns about API security. 

Secure-by-design practices and a comprehensive lifecycle management of APIs arguably help cope with the latest challenges, and composable AI-powered platforms might be the enablers of such a long-term strategy to secure data and safeguard organizations’ functions.