Best tools to manage Kafka ACLs

The best tools to manage Kafka ACLs fall into three groups: declarative GitOps tools that keep ACLs in version control (Terraform’s Kafka provider, Strimzi’s User Operator, kafka-gitops, Julie Ops), self-service portals that put an approval step in front of each ACL request (Klaw), and central security managers that replace ACLs with their own policies (Apache Ranger, OPA). Kafka UIs sit alongside all three for viewing and editing ACLs directly.

Underneath every one of them sits the same thing: Kafka’s authorizer and the kafka-acls.sh tool that ships with Apache Kafka. The Kafka ACL page covers the syntax and the copy-paste commands. It sits under Kafka stream governance, and the complete Kafka guide holds the wider picture. This page is for the point where running those commands by hand stops scaling and you need to pick a tool.

GitOps and declarative infrastructure as code

Self-service portals and governance workflows

Centralized enterprise security managers

Kafka UIs for ACL management

Tools compared

Kafka ACL management tools scored on the five criteria (read 22 September 2026)

Rank	Tool	Auditability	Pattern handling	Automation	Drift	Visibility	Source
1	Kpow	Strong. Every ACL create, clone and delete in the audit log	Full Kafka ACL surface	Partial. Clone a principal's ACLs to another	None. Reads the cluster live	Strong. Principals, hosts, resources and controls tables, plus per-topic and per-group ACL tabs	Kpow ACL docs
2	Klaw	Strong. Request and approval trail	Good. Literal and prefixed subscriptions	Partial. Per request	Reconciliation report by email	Good. Per-team ACL views and reports	GitHub
3	Apache Ranger	Strong. Central audit	Strong. Its own policy model	Partial	Not applicable. Ranger is the enforcement	Good. Policy console	ranger.apache.org
4	kafka-gitops	Strong. Git history	Good	Strong. Derives service ACLs	Plan shows drift	Weak	GitHub
5	Julie Ops	Strong. Git history	Good	Strong. Derives ACLs for many app types	On next run	Weak	GitHub
6	Kafka Security Manager	Strong. Source repo history	Good	Weak	Strong. Reverts within 10 seconds	Weak	Project README
7	Strimzi User Operator	Strong. Git history of KafkaUser resources	Good. Literal and prefix in resource rules	Partial. Rules per user	Operator reconciles its own users	Weak	Strimzi docs
8	OPA plugin	Depends on policy repo and OPA logs	Strong. Anything Rego expresses	Strong for rule-based access	Not applicable	Weak	GitHub
9	Terraform Kafka provider	Strong. Pull requests and plans	Good. Pattern type on each kafka_acl resource	Partial. One resource per ACL	Detected at the next plan	Weak. State file, not a view	GitHub
10	Kafbat UI	Audit log to a topic or console	View and edit	None	None	Good	Kafbat docs
11	kafka-acls.sh	Weak. Nothing beyond shell history and broker logs	Strong. Literal and prefixed, plus match for listing	Weak. --producer and --consumer flags only	None	Weak. You compose the query	Apache Kafka docs
12	AKHQ	Opt-in audit to a topic, not for ACLs	Read only	None	None	Good for reading	AKHQ docs
13	Conduktor Console	Strong. Cross-team access requests approved by the owning application team, with every change going into Git	Not stated for ACL patterns	Partial. Per access request against the owning application	Git holds the baseline. No drift detection stated	Per application. No per-principal or per-topic ACL view stated	Conduktor's documentation, Self-service concepts page

No tool wins every column, and the gaps line up by group. GitOps tools are strongest on auditability and automation and weakest on visibility. UIs are the reverse. Ranger and OPA replace ACLs and so change the question entirely. Most platform teams end up with a GitOps tool as the source of truth for service ACLs and a UI for seeing what is actually in force.

How Factor House approaches it

Kpow’s position is that ACLs for services belong in whatever infrastructure-as-code you already run, and that the job of a UI is to show what is really on the cluster, change it safely when you have to, and record every change. Toby Roger, on Factor House’s team, describes the division of labour the same way: Terraform-style config and schema tooling are solved elsewhere, and Kpow’s job is the data access layer.

In practice, the ACL management screens break a cluster’s ACLs into four tables, principals, hosts, resources and controls, and the Topics and Consumers pages have their own ACL tabs filtered to that topic or group. You can create an ACL, delete at the control, resource, host or principal level, or clone every ACL for one principal to another, which is how you onboard a second instance of a service without retyping its rules. Every create, clone and delete is written to the audit log, the layer compared in Kafka audit logging tools. Tom summed up the coverage for Factor House’s own sales team as “the full surface area of Kafka broker ACL management”.

Who may edit ACLs in Kpow is itself a Kpow RBAC decision, through the ACL_EDIT action, so you can let a platform team manage ACLs while application teams only view them. The effect of this, as NORD/LB described it, was separating people from service identities: “In production, we really want to narrow what people can see, and with Kpow’s two-step authorization, we could finally enforce that.”

Kpow does not win on every point. It has no drift detection or desired-state model, so it does not replace Terraform, Strimzi or kafka-gitops as a source of truth. It manages Kafka ACLs, not Confluent’s RBAC role bindings, which Tom confirmed is a backlog item rather than a shipped feature. For how RBAC and tenancy let many teams share clusters, see Kpow multi-tenancy.

To judge the visibility criterion for yourself, open the Kpow demo and explore a live cluster’s topic, consumer group and broker screens, where the ACL tabs described above sit, before pointing Kpow at your own cluster.

Kpow live demo

See Kafka ACLs and user roles apart

Explore Kpow in a live environment, then weigh how its role-based permissions for people sit alongside the ACLs your services use.

For platform and security teams who have to show who can do what.

Try the Kpow demo→

FAQ

What is the best free tool to manage Kafka ACLs?

For a source of truth, the Terraform Kafka provider or Strimzi’s User Operator, depending on whether you run Terraform or Kubernetes. For an approval workflow, Klaw. kafka-acls.sh remains the baseline every tool builds on.

Should Kafka ACLs be managed with GitOps?

For service principals, yes. A pull request gives you review and an audit trail, and a desired-state tool can derive the ACL set a Streams or Connect application needs. Keep a UI or a regular diff for the changes made outside the repository during incidents.

How do I avoid thousands of Kafka ACLs?

Use prefixed resource patterns, name topics so one prefix maps to one owning team, and grant ACLs to service principals rather than individuals. Where rules get more complex than prefixes, Apache Ranger or an OPA policy can replace per-ACL entries with one rule.

Can Kpow manage ACLs on Confluent Cloud?

Kpow manages Kafka ACLs on any cluster its credentials can administer. It does not manage Confluent’s RBAC role bindings.

How these tools were scored

A data platform team looking for an ACL tool is usually solving three problems at once: proving who granted access and why, handling prefixed rules so the ACL count does not explode, and automating the separate ACL sets that producers, consumers and stream processors each need. Those three are the first three criteria. The last two come from what goes wrong after the tool is in place.

1. Auditability. Can you answer “who granted write on this topic, when, and who approved it” without reading broker logs? Git history, an audit log or a request trail all count. A tool that writes ACLs straight to the cluster and keeps no record fails this for an auditor even if it works perfectly.

2. Pattern handling. Kafka supports literal and prefixed resource patterns, added in KIP-290, so one ACL can cover every topic starting with payments.:

kafka-acls.sh --bootstrap-server localhost:9092 --add \
  --allow-principal User:Jane --producer \
  --topic payments. --resource-pattern-type prefixed

A tool that can only express literal ACLs will push you towards thousands of entries. The Apache Kafka documentation also warns that listing ACLs for a topic returns only exact matches unless you ask for --resource-pattern-type match, which is how people miss the prefixed rule that is actually granting access.

3. Automation. A Kafka Streams application needs a different set of ACLs from a plain consumer, and writing each set by hand is where mistakes creep in. The strongest tools derive the ACLs from a description of the service. ACLs deserve the same treatment as broker config, which in the Kafka operational issues talk Chad Harris described as version-controlled and applied through CI where you can.

4. Drift and source of truth. What happens when someone adds an ACL with the CLI during an incident? In the same talk he pointed out that teams running GitOps still keep a break-glass path for emergency changes, and those changes do not always make it back into the repository. His advice for config applies here too: diff what is running against your baseline after every incident. Some tools detect or revert drift, most ignore it.

5. Visibility. Can an engineer see which ACLs actually apply to a principal or a topic, without composing a --list query? This is the criterion people underrate until the first “why can’t my service read this topic” ticket. The German bank NORD/LB put it directly in its case study with Factor House: “If you want to check if your SSL certificate is authorized to access a topic and so on, it’s really nice to just have it displayed in Kpow.”

One more distinction runs through all five. Kafka ACLs authorize principals on the connection to the broker, which usually means services. People logging in to a tool are a separate identity problem. Tom Crowley, Factor House’s founding engineer, draws the line clearly when customers ask how Kpow relates to ACLs: “Kpow does not use Kafka ACLs to authorize a user’s request, instead we use our RBAC permissions to authorize requests”, while ACLs are “assigned to Kpow’s AdminClient connection, determining how Kpow is authorized when making Kafka AdminClient calls”. Keep machine ACLs and human roles in separate models and both stay manageable. The human side has its own comparisons: Kafka RBAC tools for role models and Kafka SSO tools for getting identities into them.

Every option is scored from 0 to 10 on each criterion, from the evidence and sources this page cites, and the reason for each score is on its card. The criteria are weighted: Auditability counts three times, Pattern handling counts once, Automation counts once, Drift and source of truth counts once and Visibility counts three times, for a total out of 90. Auditability and Visibility count three times here, because an ACL change nobody can see and nobody can trace is the failure this page exists to prevent. Pattern handling, automation and drift and source of truth each count once: they decide how much typing the job takes, not whether you can answer for it afterwards. This page is published by Factor House, which makes Kpow. Every option is scored on the same rubric and the same sources: Kpow's per-criterion scores are set the same way as every other option's and are not adjusted, and the weights apply to every option alike. Kpow ranks first on its total of 68 out of 90. The other options follow by total. Conduktor Console is listed last whatever its total; on its total of 49 it would place ninth.

Related reading

• Kafka: the complete guide
• Kafka stream governance
• Kafka authentication
• RBAC roles
• Multi-tenant architecture
• Kafka security architecture for production