A service mesh is an infrastructure layer that’s designed to securely handle communication between your microservices. It works through a distributed network of lightweight proxies, and rather than having to handle network concerns at the application code level, you get a set of tools that help route traffic and secure and monitor the exchanges.
Without a service mesh in the mix, your microservices system can get messy fast – each team will have to figure out API security on their own (think inconsistent authentication and authorization, patchy traffic encryption, and a bunch of duplicated logic for error handling and routing). In other words, a service mesh saves time and streamlines logic by centralizing transport security and policy enforcement across all microservices.
But with all the upsides, service meshes pose big security concerns themselves. The control plane is an attractive target for attackers: Compromising it means controlling the entire service ecosystem. Plus, misconfigurations are fairly common, and one wrong authorization policy can expose sensitive services or break legitimate traffic flows.
In this article, we'll walk you through the finer workings of service meshes, the security wins they bring to microservices, the challenges you'll face when deploying them, and how they compare to API gateways in your existing cloud setup.
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The security benefits of service meshes
There are a lot of security benefits that come from adopting a service mesh:
Automated mTLS encryption for all inter-service communication without code changes
Transparent certificate provisioning, rotation, and validation
Cryptographic protection across the service mesh microservices ecosystem
Centralized policy enforcement
Deep integration with Kubernetes and other container orchestration platforms, allowing it to tap into existing workload identity systems, namespace boundaries, and native security primitives
Automatic service discovery and injection of sidecar proxies, eliminating the need for manual configuration of each new deployment
Rich observability that goes way beyond standard monitoring to capture the complete communication graph—who talks to whom / how often, what fails and why
Another payoff is easier audit evidence. Frameworks such as PCI DSS and HIPAA expect encryption in transit and access controls; mesh mTLS, authorization policies, and telemetry help produce evidence that service communications were authenticated, authorized, and encrypted—though meshes alone don't satisfy all framework requirements.
How service mesh architecture paves the way for secure microservices communication
Sidecar proxy model for traffic interception
We’ve seen that the sidecar proxy model forms the foundation of the service mesh security architecture, but here’s how it works: Lightweight proxies (typically built on Envoy) are placed adjacent to each application container in order to intercept all network traffic without any additional application-level changes. In Kubernetes, the sidecar runs in the same pod as the application, sharing the network namespace and volumes while maintaining container-level process isolation through Linux namespaces and cgroups.
Envoy service mesh proxies offer both Layer 4 capabilities and Layer 7 capabilities:
Layer 4:
TCP load balancing
Connection management
Layer 7:
Inspection of HTTP headers
Request modification abilities
Enforcement of protocol-specific policies
Control plane policy management
The control plane is a central console for managing sidecar proxies, which allows for consistent policy management. It’s at this level where components like Istio's istiod or Linkerd's controller distribute configuration updates and manage service discovery.
Data plane security
mTLS: Service meshes use mutual TLS (mTLS) to establish bidirectional trust and prevent attacks on east–west traffic (such as man-in-the-middle attacks).
Certificates: A service mesh requires both communicating parties to furnish valid certificates, which are automatically provisioned, rotated, and validated through systems like SPIFFE/SPIRE. (SPIFFE assigns a URI to every workload so that each has a unique identifier, regardless of its network location.) In Envoy-based meshes, short-lived X.509 certificates are rotated via Envoy's secret discovery service (SDS); other meshes provide equivalent mechanisms—for example, Linkerd uses its own Identity service for certificate management.
Access control: Service meshes enforce fine-grained access control by leveraging workload identities. For instance, Istio uses its Authorization Policy resources and Envoy's RBAC filters to define who can access what services and under which conditions.
Zero trust networking: A service mesh employs zero trust principles in that every request must include cryptographic proof of identity, and the authorization policies determine access rights based on that authenticated identity rather than network location.
Beyond workload authentication, you can validate end-user identity by processing JWT or OIDC tokens at mesh ingress gateways or via Envoy filters, then propagate that identity to downstream services using HTTP headers or peer identity attributes for fine-grained authorization.
Graph-aware posture analysis
A service mesh can record the identity relationships, certificate chains, and policy distributions across the service topology to identify authorization gaps and excessive permissions. All this info is used to generate rich telemetry about how services interact with one another, which can be fed straight into a security graph that models attack paths and blast radius scenarios across the microservices ecosystem.
Service meshes vs. API gateways in cloud security architecture
Service meshes and API gateways solve different problems but work better together than in isolation.
A service mesh focuses on the traffic between internal microservices (aka east–west traffic). They enforce zero trust and authentication across the board to maintain uninterrupted and secure service-to-service communication.
On the other hand, gateways handle external clients trying to connect to your infrastructure (aka north–south traffic) and excel at packaging APIs into full products by offering usage analytics and, in some platforms, monetization tools. When securing public APIs, gateways also manage the authentication flows, rate limiting, and request transformation that external clients demand.
Most mature architectures will deploy both technologies: The API gateway provides perimeter security and client-facing controls at the edge, while the service mesh delivers service-to-service encryption, authentication, and resilience internally.
Service meshes and API gateways also complement each other when it comes to security control placement. A service mesh can expose ingress and egress gateways that participate in north–south paths, but you still need to rely on dedicated API gateways that own the advanced Layer 7 controls, developer portals, and billing integration that external-facing APIs require.
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Service mesh implementation challenges and operational overhead
Security risks
Service meshes tend to create their own attack surface. The biggest target is the control plane—once it’s compromised, attackers can control your entire service ecosystem, issuing rogue certificates and manipulating routing rules. Misconfigurations are just as big of a threat and surprisingly common too: One wrong authorization policy can expose sensitive services or kill legitimate traffic entirely.
To top it all off, the steep learning curve amplifies risks because teams often treat the mesh as a complete security solution while neglecting foundational network policies and cloud-native controls. This creates gaps where teams think they're protected but aren't.
Performance hits
Perhaps the biggest obstacle to adopting a service mesh is that it can torpedo application performance. A 2023 study shows that latency increases up to 269% and CPU consumption jumps by 163% in some configurations (note that actual impact varies based on workload patterns, proxy settings, and feature usage).
Teams dealing with Kubernetes middleware risks face greater complexity when service mesh enters the picture, with the performance tax getting steeper (think latency increases and CPU consumption jumps that require careful tuning). Sidecar-less options like Istio Ambient Mode show promise with materially lower overhead than traditional sidecars in early benchmarks, but they still add overhead versus native networking—although the actual gains will depend on your traffic patterns and feature usage. Linkerd can minimize performance impact thanks to its lightweight, Rust-based sidecar proxy and streamlined design.
Configuration complexities
Envoy filter chains can quickly become complicated, considering each service has its own custom timeout values, retry policies, and circuit breaker settings. Managing these configurations, especially without proper governance, can create massive drift between environments.
Problems with legacy integrations
Older VMs and databases can’t host sidecar containers, which forces engineers to find workarounds through various mesh expansion features. Unfortunately, these workarounds have their own security implications, which teams aren’t always prepared for.
Troubleshooting across distributed enforcement points
Debugging issues across distributed sidecar proxies requires expertise and specialized tools because there’s no guarantee the issues are localized: Problems may span application code, proxy configuration, control plane policies, and underlying network infrastructure. Traditional debugging approaches fall short when requests go through multiple enforcement points, though there are Kubernetes security tools that can help teams manage some of these operational challenges.
How Wiz supports security visibility and risk management in service mesh environments
Service meshes provide core controls for service-to-service communication, such as identity, mTLS, and traffic policy enforcement. Wiz complements these capabilities by helping teams gain visibility, validate assumptions, and understand risk across the cloud infrastructure, Kubernetes environment, and APIs that operate alongside a service mesh. Rather than enforcing mesh behavior, Wiz focuses on surfacing misconfigurations, vulnerabilities, exposure paths, and API risks that can weaken the security guarantees teams expect from their mesh.
Wiz helps teams by:
Providing cloud-to-code context for mesh components and workloads
Wiz identifies vulnerabilities and risky configurations in service mesh components such as Envoy proxies or mesh controllers and traces them back to infrastructure-as-code definitions and owning repositories. This connects production findings to source and configuration context, supporting remediation without requiring developers to deeply understand mesh internals.Performing agentless visibility across mesh control and data planes
Using cloud and Kubernetes APIs, Wiz scans mesh control plane components (such as istiod and Linkerd controller/destination services) and data plane proxies (Envoy and linkerd-proxy) without deploying in-workload agents or impacting mesh performance.Correlating mesh workloads with broader cloud risk context
Wiz’s Security Graph correlates mesh-related workloads with cloud identities, effective network exposure, and sensitive data access. This helps identify situations where services protected by mesh controls may still present risk due to cloud-level misconfigurations, excessive IAM permissions, or unintended exposure paths.Highlighting control plane posture issues that increase blast radius
Wiz can surface mesh-related risks such as controllers running with excessive Kubernetes privileges, overly broad CRD write access, or weaknesses in supporting components like admission webhooks, etcd, and the Kubernetes API server that directly affect mesh integrity.Identifying potential mesh bypass and unintended exposure paths
By calculating effective exposure across cloud security groups, network ACLs, Kubernetes NetworkPolicies, and ingress configurations, Wiz helps teams understand where traffic paths may bypass intended mesh enforcement due to configuration drift or cloud networking gaps.Restoring API visibility inside encrypted service meshes
Wiz Sensor discovers APIs running within widely used service meshes – like Istio (sidecar and Ambient) and Gloo – by observing unencrypted HTTP traffic before encryption, even when mTLS is enabled. Wiz combines this discovery with agentless analysis and API gateway signals to assess API risk aligned with the OWASP API Top 10, correlating findings with cloud exposure, identity permissions, and data sensitivity.
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