New challenges in microservice security
In 2026–2027, securing microservice architectures requires proactive approaches. The growing complexity of cyber threats makes traditional security methods, such as static rules and signature analysis, insufficient. According to the ENISA Threat Landscape report, phishing remains the leading initial access vector, used by attackers to compromise credentials and bypass basic authorization. In complex microservice environments, attacks on legacy protocols or misconfigured APIs are also on the rise.
The role of AI policies in API gateways
An API gateway acts as a single entry point for all requests, enabling centralized enforcement of security policies. Integrating AI policies transforms it into an intelligent security hub, providing the following capabilities:
- Automated anomaly detection: AI models analyze traffic and detect deviations from the norm (unusual request volumes, suspicious geographic locations).
- Proactive response: if a threat is detected, the system automatically blocks requests or restricts access, reducing response times compared to manual processes.
- Protection of AI models themselves: input data monitoring prevents injection attempts or data poisoning.
According to the NIST AI RMF 1.0 guidelines, implementing artificial intelligence requires evaluating not only model accuracy but also the context of use, reliability, and resilience to manipulation. This helps prevent a high volume of false positives during seasonal peak loads.
A practical use case in the financial sector
For large financial institutions, securing disparate customer data in compliance with GDPR, PCI DSS, or NIS2 is critical. In such scenarios, an API gateway with AI policies analyzes user behavior in real time. For instance, if an attempt is made to access sensitive data from an unusual IP address, the system prompts for additional authentication or blocks the transaction. This enables centralized protection of integrated systems, including those built on low-code platforms like UnityBase.
What it means for companies
For the software industry and digital businesses, the shift toward AI-driven API security means that traditional static defense mechanisms are no longer viable. Failing to adapt exposes organizations to severe regulatory penalties under GDPR and NIS2, as well as critical data breaches. Conversely, integrating AI policies allows companies to achieve faster incident response times, optimize operational costs for monitoring, and safely leverage low-code integrations.
Action plan
To secure microservice architectures against modern threats, organizations should take the following practical steps:
- Audit existing APIs to identify vulnerabilities and legacy protocols.
- Select and deploy an AI-enabled API gateway capable of real-time anomaly detection.
- Develop specific security policies aligned with NIST AI RMF 1.0 guidelines to minimize false positives.
- Integrate the gateway with existing SIEM and IAM systems for centralized threat management.
Prepared by a Software Ukraine member. Original publication.