# Top Application Detection and Response (ADR) Tools for 2026

_Application detection and response tools catch attacks inside running apps. Compare the top ADR tools for 2026, how they work, and how to pick the right one._

## **What is application detection and response (ADR)?**

[Application detection and response (ADR)](https://www.wiz.io/academy/detection-and-response/application-detection-and-response) is security that detects and responds to attacks inside a running application. In plain terms, it watches how your applications behaves in production, not just how code looks before it ships.

ADR tools use runtime tools to observe application behavior. (eBPF is a Linux technology that safely runs small programs inside the kernel to watch activity.) These sensors flag anomalous execution, such as a SQL injection, a deserialization exploit, or an unexpected process or network call, and then route a response.

Because the tool sees the live call stack and which library functions actually run, it can tell suspicious behavior from expected actions. Older tools leave a gap here: SAST and DAST review code before and during testing, a WAF inspects traffic at the edge, and EDR watches the host operating system. None of them sit inside the application process itself, which is exactly where modern[ runtime cloud security](https://www.wiz.io/academy/cloud-security/runtime-cloud-security) has to operate.

The stakes are easy to see in the data. Wiz research found that[ 26% of breaches come from exploiting public-facing applications](https://www.wiz.io/reports/cloud-attack-report-2025), which is precisely the ground ADR is built to defend.

## **Why ADR matters now (the runtime blind spot)**

Attackers have changed where they aim. Instead of hunting for a weak perimeter, they target vulnerabilities in running apps and libraries, the kind of flaw a scanner often ranks as low priority until it gets hit in production. Log4Shell was the wake-up call, and the pattern has only grown since.

A deserialization flaw buried in a dependency shows why this matters. A scanner notes the vulnerable package but rates it low, because nothing in the static code looks reachable. In production, a crafted request walks straight into that path, triggers code execution, and gives the attacker a foothold. The flaw only becomes reachable once the application runs.

SAST and DAST read code, a WAF watches traffic at the edge, and EDR watches the host, but none of them see what happens inside the running application process at the moment an exploit fires. That gap lines up with where attackers are spending their effort across every industry. IBM's 2026 X-Force Threat Intelligence Index reported that exploitation of public-facing applications [surged 44% year over year](https://www.ibm.com/think/x-force/threat-intelligence-index-2026-securing-identities-ai-detection-risk-management), and vulnerability exploitation has become a leading initial-access vector. These are global, all-industry numbers, but they point to the same place attackers land first, the application itself, which is where you need eyes at runtime.

In practice, this means a vulnerability you deprioritized can become the entry point for lateral movement and a wider blast radius. Watching the application at runtime closes that gap, which is why ADR now sits next to[ cloud threat detection tools](https://www.wiz.io/academy/detection-and-response/threat-detection-tools) in most SOC plans.

## **Criteria for evaluating modern ADR tools**

A good ADR tool proves itself by catching real attacks with minimal impact on application performance. Use these five criteria to compare options on the same footing.

1. **Deployment overhead and performance impact:** Check whether the tool is agent-based or agentless, which language runtimes it supports (Java, Node.js, Go, Python, .NET), and how much CPU and memory it adds.
1. **Exploit reachability and trigger validation:** Confirm whether the tool proves a payload actually reaches and activates a real execution path, instead of only noting that a vulnerable package exists.
1. **Precision and alert correlation:** Look for tools that suppress unexecutable recon noise and send correlated, actionable telemetry to the SOC, which keeps the false positive rate and analyst fatigue low.
1. **Automated response:** Weigh the response options, from inline blocking and microservice or network isolation to rate limiting and temporary virtual patching.
1. **Contextual enrichment:** The best tools trace a runtime incident back to the specific repository, commit, container image, and cloud role, which connects[ product security across code and runtime](https://www.wiz.io/academy/application-security/product-security).

Score each tool against the same rubric, because a strong demo means little if the detections cannot tell your team where a threat came from. In practice, the deployment and context criteria tend to decide the winner, since a tool that is slow to roll out or vague about root cause rarely survives a real incident. Operational fit counts too, so weigh the pricing model and log retention limits before you commit, since those details often decide which tool holds up in day-to-day use. Teams that also invest in[ detection engineering](https://www.wiz.io/academy/detection-and-response/detection-engineering) get more value from whichever tool they pick.

## **Top application detection and response tools for 2026**

Solutions are listed in no particular order. The table below gives a quick view, and the sections after it add detail on each option.

| Vendor | Best for | Deployment model | Notable strength |
| --- | --- | --- | --- |
| Wiz | ADR inside a unified cloud security platform | Agentless CDR plus lightweight eBPF and container sensors | Code-to-cloud context on the Security Graph |
| Oligo Security | Open-source and library runtime visibility | eBPF runtime sensor | Library-level reachability |
| Contrast Security | Developer-centric runtime protection | In-app instrumentation (IAST heritage) | Deep developer context |
| Miggo Security | API-heavy microservice apps | Distributed tracing, no code changes | Application flow mapping |
| Kodem | Prioritizing truly reachable risk | eBPF sensor | Exploit-trigger defense with dynamic SCA |
| Raven | Low-overhead runtime observability | Out-of-band eBPF | Rapid root-cause isolation |

### **Wiz**

Snapshot: Wiz delivers unified runtime detection that correlates in-cluster application anomalies with the cloud asset graph, toxic combinations, and the source repositories behind them.

The difference starts with how Wiz collects signals. It combines 100% agentless scanning with a lightweight eBPF runtime sensor on Linux VMs and Kubernetes nodes. For serverless containers, a dedicated sidecar extends that runtime coverage. When the sensor spots something off inside a running app, that signal does not land as an isolated alert.

- **Agentless and runtime together:** rapid discovery pairs with the eBPF sensor, so detection does not depend on heavy agents on every workload.
- **Code-to-cloud context:** the Wiz Security Graph enriches each detection with who did it, what access they had, whether the asset is internet-exposed, and the blast radius if it is real.
- **Trace-back to source:** a runtime detection links to the exact repository, image, and developer, so teams can patch the flaw where it was introduced.
- **Blue Agent and analyst-led containment:** the AI agent investigates triggered alerts against the graph and returns a verdict, confidence score, and summary. Analysts or defined response policies then isolate a workload or revoke a role.
- **Exploitable and important risk first:** Wiz ranks detections by real cloud context, not just code reachability, factoring internet exposure, identity, data sensitivity, and blast radius so teams fix what truly matters.

Because every signal arrives with cloud and code context attached, analysts can act on a detection without first hunting across separate tools to understand it. That connection between application behavior, cloud exposure, and the code that shipped it is what sets the approach apart. Best for: teams that want ADR as part of a unified cloud security platform rather than a standalone point tool.

### **Oligo Security**

Snapshot: Oligo focuses on eBPF-based runtime application security, with an emphasis on library-level behavior and reachability.

- **Library behavior monitoring:** it profiles how open-source libraries actually run and flags deviations from their expected behavior.
- **Reachability analysis:** it helps separate vulnerable packages that run from those that sit unused, which trims the patch backlog.
- **Low-friction deployment:** the eBPF approach adds visibility without changing application code.

Best for: teams that want sharp visibility into open-source and library behavior at runtime.

### **Contrast Security**

Snapshot: Contrast uses in-app instrumentation rooted in its IAST heritage to give developers deep context and block exploits automatically.

- **In-app instrumentation:** sensors run inside the application to watch real request flows and data handling.
- **Developer context:** findings point back to the specific code path, which fits teams that want AppSec close to engineering.
- **Automated exploit blocking:** it can stop certain attacks inline as they execute.

Best for: AppSec teams that want developer-centric runtime protection tied to their codebase.

### **Miggo Security**

Snapshot: Miggo builds on a distributed tracing architecture that maps application flows and detects API and application abuse without changing code.

- **Application flow mapping:** tracing shows how requests move across services, which highlights abnormal paths.
- **API abuse detection:** it watches for misuse patterns across API-heavy systems.
- **No code modification:** coverage comes from tracing rather than in-app agents.

Best for: API-heavy, microservice applications that need flow-level visibility.

### **Kodem**

Snapshot: Kodem combines eBPF exploit-trigger defense with dynamic software composition analysis and runtime call-stack monitoring.

- **Exploit-trigger defense:** it watches for the moment a payload activates a real execution path.
- **Dynamic SCA:** it confirms which dependencies load and run, so reachable risk rises to the top.
- **Call-stack monitoring:** runtime visibility into the call stack helps validate whether a flaw is truly exploitable.

Best for: AppSec teams that want to confirm which dependency vulnerabilities are actually reachable in running code before they patch.

### **Raven**

Snapshot: Raven provides out-of-band eBPF application observability centered on library call tracing and fast root-cause isolation.

- **Out-of-band design:** observability runs alongside workloads to keep overhead low.
- **Library call tracing:** it follows how libraries are called at runtime for clearer signal.
- **Rapid root-cause isolation:** it helps teams pinpoint where an issue starts during an investigation.

Best for: teams that want low-overhead runtime observability.

## **Detect and respond at runtime with Wiz**

Step back through the buying criteria and one theme repeats: a strong ADR tool has to see real exploit behavior, keep noise low, respond fast, and tell you where the threat came from. Wiz Defend ties all four together in a single platform.

It starts with discovery. Agentless scanning maps the environment fast. The lightweight eBPF sensor (typically[ 1 to 3% CPU overhead](https://www.wiz.io/academy/cloud-security/runtime-cloud-security)) adds in-process visibility on Linux VMs and Kubernetes nodes, and a dedicated sidecar covers serverless containers. When the sensor catches an anomaly inside a running app, the detection does not stand alone. The Wiz Security Graph enriches it with identity, exposure, and blast-radius context, which satisfies the precision and correlation criteria in one move.

From there, the signal traces back to the exact code, repository, and developer that introduced the flaw. Your team can then patch the source while containing the live threat. The Blue Agent investigates the alert against the graph. It returns a verdict, confidence score, and investigation summary, which cuts the time analysts spend on manual triage.

Wiz correlates runtime signals with graph context, so each detection arrives with identity, exposure, and blast-radius already attached. Analysts work from connected events, which keeps alert volume manageable and speeds up triage.

[Get a demo](https://www.wiz.io/demo) to see how Wiz connects runtime application signals to cloud and code context in one graph.

## **Frequently asked questions about ADR tools**

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[View on wiz.io](https://www.wiz.io/academy/detection-and-response/application-detection-and-response-adr-tools)