M2M authentication for internal services and external APIs

In an AI-powered world, non-human identities continuously communicate—both internally, within your own infrastructure, and externally, with third-party partners or clients. This means that machine-to-machine (M2M) authentication too, needs to happen in both cases.

Authenticating internal microservices and external API calls have fundamentally different security requirements, operational constraints, and trust models, so you need to ensure that you have a clear way of authenticating both.

Kubernetes has an insightful blog dedicated to authenticating internal microservices. The title of the blog says it all: "Consider All Microservices Vulnerable — And Monitor Their Behavior”.

Here’s a tl;dr table for you from the blog.

If you’d like to go into more detail on authenticating APIs, we have a cool guide for you that sums up the major authentication methods.

The two worlds of M2M authentication in B2B SaaS

Internal service-to-service authentication

Internal authentication occurs within your infrastructure, such as a Kubernetes cluster or a service mesh. For instance, consider a billing service accessing user profile data. These internal interactions occur within a trusted, controlled environment, significantly reducing external threats and enabling a smoother approach to authentication.

External API authentication

External authentication involves third-party integrations, such as a third-party analytics tool accessing your customer data. Here, trust levels are considerably lower because these external services are outside your direct control, requiring stronger authentication, detailed auditing, and comprehensive security measures.

Example: Auth in a SaaS analytics company

Imagine a SaaS analytics platform, Saasalytics:

Internally, it includes services like user management, event processing, and dashboards. Externally, it exposes APIs for customer data uploads, account management, and reporting.

Internal authentication approach: Optimizing for efficiency and security

For internal interactions, Saasalytics uses solutions like SPIRE with mutual TLS (mTLS), ensuring secure, efficient verification of service identities. Short-lived certificates that rotate automatically (e.g. every 24 hours) further enhance security and performance, minimizing operational overhead.

Internal authentication prioritizes performance without sacrificing security.

This approach suits environments where latency must be minimized, and rapid, automated credential management is feasible. Saasalytics authenticates its event processing and dashboard microservices using mTLS, ensuring real-time analytics with minimal latency and secure internal communications without external exposure.

External authentication approach

Externally, Saasalytics adopts OAuth’s Client Credentials Flow enabling structured, standardized authentication. It implements scoped JWT tokens to tightly control access, sets stringent token expirations (typically one hour), and enforces rigorous logging, rate limiting, and traceability to mitigate risks.

Given the inherently lower trust of external systems, external authentication employs defense-in-depth strategies—rigorous rate limiting, systematic token rotation, explicit consent management, and robust auditing capabilities. A notable example is payment platforms like Stripe, which enforce strict OAuth-based authentication, scope limitations, and detailed monitoring to prevent misuse and fraud.

For external API interactions, Saasalytics adopts OAuth 2.0, allowing third-party analytics tools or customer applications to securely access data through scoped JWT tokens.

Tokens are:

• Strictly limited to permissions such as ^{read:analytics-data} or ^{write:customer-events}
• Expire hourly
• Subject to rate-limiting and logging

These measures ensure secure, auditable, and controlled access.

Key differences

Common mistakes and how to avoid them

When you need to authenticate in two different worlds, ensure your worlds don’t collide. Here are some best practices:

• Not reusing internal credentials for external authentication to prevent vulnerabilities.
• Ensuring internal services authenticate independently, not relying solely on perimeter security.
• Rotating and expiring credentials systematically, especially for external APIs.
• Maintaining rigorous authentication practices even in development to avoid security complacency.

AI agents and MCP authentication considerations

Ask your team these questions and ensure that these use cases are covered when you build authentication.

• Are you exposing APIs specifically for AI-driven workflows and agents?
• Can your authentication approach support MCP-compliant flows and standards?
• Do you provide clear, user-consented OAuth access for AI agents?
• How will you enforce scoped access specifically tailored for AI automation tasks?

The way forward

The security of your SaaS platform relies on clearly understanding and differentiating between internal and external machine-to-machine authentication. Rather than applying a one-size-fits-all approach, technical leaders must proactively assess their specific scenarios:

• Internally, optimize authentication to reduce latency, leveraging mTLS and frequent credential rotation to maintain both security and performance.
• Externally, adopt rigorous authentication protocols like OAuth 2.0, enforce strict token management policies, and implement comprehensive logging and auditing for transparency and compliance.
• Prepare for AI-driven workflows by embracing standards such as MCP. Ensure your authentication frameworks support dynamic, user-consented access specifically tailored for AI agents and automated workflows.