How does Spring AI integrate with Spring Security for securing AI endpoints?

Spring AI does not ship its own security layer — it relies entirely on Spring Security, which is the standard approach for all Spring Boot APIs. Securing AI endpoints is exactly the same as securing any REST endpoint, with a few AI-specific considerations around rate limiting, API key management, and audit logging of AI interactions.

Standard Spring Security configuration for an AI endpoint:

@Configuration
@EnableWebSecurity
public class SecurityConfig {
    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
        return http
            .authorizeHttpRequests(auth -> auth
                .requestMatchers("/ai/admin/**").hasRole("ADMIN")
                .requestMatchers("/ai/chat").authenticated()
                .anyRequest().permitAll())
            .oauth2ResourceServer(oauth2 -> oauth2.jwt(Customizer.withDefaults()))
            .build();
    }
}

AI-specific security considerations:

• Rate limiting per user — LLM calls are expensive; use Bucket4j or Spring Cloud Gateway rate limiting to cap requests per authenticated user and prevent abuse.
• Audit logging — Log each AI interaction (user ID, prompt hash, response length, model used) for compliance. A custom SimpleLoggerAdvisor variant can write structured audit entries to a separate audit log rather than application logs.
• System prompt confidentiality — Never expose your system prompt in error messages or API documentation. Log it only to secured audit sinks.
• API key rotation — Store provider API keys in Spring Cloud Vault or AWS Secrets Manager and rotate them regularly. Never commit keys to source control.