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Application detection and response (ADR) is an approach to application security that centers on identifying and mitigating threats at the application layer.
Application detection and response (ADR) is an approach to application security that centers on identifying and mitigating threats at the application layer. It involves real-time monitoring of an application’s context to detect malicious activity and potential attacks.
ADR tools operate directly within the application environment, rather than monitoring the perimeter or the network layers. By using modern instrumentation methods, ADR provides real-time visibility across the entire software stack, continuously scanning for anomalous behavior in application components.
When threats are detected, ADR solutions alert security teams with highly contextual information to help them address issues. The ultimate goal of ADR is to detect and mitigate application attacks before they escalate.
For decades, we’ve relied on traditional methods to secure the application layer, including antivirus software, network firewalls, andweb application firewalls (WAFs). However, as applications have become more complex, these solutions have proven inadequate.
The main problem with antivirus technologies is that they generally struggle to keep pace with rapidly changing attack vectors. Firewalls and WAFs have downsides too because they are perimeter-based solutions that mainly focus on network traffic.
Given these limitations, application detection and response has emerged as the natural evolution of traditional security methodologies.
For decades, we’ve relied on traditional methods to secure the application layer, including network and web application firewalls (WAFs). Yet as applications have grown more complex and distributed, these solutions have shown their limitations.
WAFs, while popular for their easy setup and network-level protection, have limited effectiveness in dynamic, complex environments. They operate as perimeter-based solutions, relying on rule-based filtering of network traffic, which often devolves into an unmanageable set of constantly changing rules. This approach struggles to keep up with sophisticated, evolving threats, often only deterring low-level attacks.
Later, Runtime Application Self-Protection (RASP) was introduced to address these shortcomings by integrating directly into applications, providing deeper, runtime-level protection and visibility into application-layer threats. However, RASPs require extensive developer involvement, creating operational friction. They also lack visibility into the broader infrastructure and container environments, which limits their effectiveness in modern cloud-native setups.
This gap has led to the emergence of Application Detection and Response (ADR). ADR strikes a balance between WAF’s ease of implementation and RASP’s deep application insight. By leveraging runtime observability and behavior-based threat detection, ADR provides the best of both worlds. It can detect and respond to complex, in-app threats in real time, addressing vulnerabilities that become active only after deployment.
With ADR, organizations gain in-depth application protection designed for today’s complex environments. It represents a new standard in application security—one that adapts to the growth of open-source adoption, demands of cloud-native architectures, and the evolving threat landscape.
As the name suggests, application detection and response focuses on detecting application attacks and responding to them. ADR systems address two primary attack vectors:
External threats: Threat actors attempting to compromise deployed applications from the outside, such as by sending malicious requests.
Internal threats: Supply chain attacks where attackers inject malicious code by compromising open-source libraries used in the application.
Application detection is responsible for diagnosing potential security threats at the application level—both internal and external. As we’ve seen, rather than scanning network traffic like firewall solutions, ADR tools operate within application environments.
Once a threat is discovered, the response phase begins. This includes providing thesecurity operations center (SOC) with detailed threat intelligence, making it easier to understand what happened and helping prevent future incidents. Some advanced ADR tools even offer automated mitigation capabilities.
Next, let's explore the core functionalities provided by a typical ADR solution.
Application monitoring
Application monitoring is the process of continuously observing an application's performance and behavior to ensure its reliability and security. This strategy is implemented throughsoftware instrumentation, which requires embedding monitoring code within the application codebase or utilizing tools that track code execution, data flows, and user interactions in real time.
ADR employs application monitoring to oversee application context at runtime and discover anomalous behavior. This constant oversight enables ADR tools to gather exhaustive information about the application's security posture.
Profiling of Open-Source Libraries
Some ADR vendors profile open-source libraries, tracking their behavior under regular conditions. By establishing a baseline profile of these libraries, these ADR tools can detect any deviations or unexpected behavior, which may indicate that an attacker has compromised and modified the library code. This approach helps address supply attacks where attackers manage to insert malicious code into third-party components commonly used by developers.
Application monitoring aims to collect data that can be used to identify deviations from established regular behavior. To do so, ADR solutions rely on behavioral analysis and machine learning (ML) to evaluate user actions and other interactions. For example, unusual API requests or unauthorized login attempts can generate alerts for further investigation.
Threat analysis and alerting
After detecting a potential application attack, ADR systems conduct in-depth threat analysis to evaluate severity and possible impact. During this phase, the ADR tool correlates the detected anomaly with known attack patterns and vulnerabilities.
Threat analysis—combined with the contextual information gathered through application monitoring—generates actionable alerts. Security teams can use these insights to understand the nature of the security issue, streamlining the investigation process and guaranteeing a swift response.
Automated response
Some advanced ADR solutions include automated response features to immediately block threat actors, isolate compromised systems, or enforce strengthened security policies without manual intervention.
Why is ADR important?
Here are the main benefits of integrating ADR into your application security pipeline:
Enhanced application-level threat discovery: Advanced behavioral analysis enables more effective application threat discovery compared to traditional security measures. The ability to observe sophisticated attack patterns as they unfold allows you to take a proactive security stance against emerging risks.
Improved application resilience: Ongoing monitoring andthreat detection create a more resilient security environment at the application layer, providing the security operations center with all the necessary information to gain a comprehensive overview of what’s happening within the application.
Reduced false positives: By constantly analyzing application behavior, ADR manages to accurately distinguish between real attacks and non-threatening anomalies. Narrowing your attention to genuine risks leads to improved efficiency in security operations.
Limited attack surface: ADR ensures that application attacks are reported while they are occurring or as soon as threat actors attempt to gain access to the system. This immediacy facilitates automatic responses or prompts manual intervention that limits successful breaches and minimizes the impact of ongoing cyberattacks.
Faster incident response: By providing precise information about the threats and context of security events, ADR supports security teams in quickly assessing and resolving incidents. The result? Reduced downtime and time spent troubleshooting, ultimately improving overall business continuity and cost savings.
Common use cases of application detection and response
Detecting and responding to anomalous behavior
By embedding monitoring capabilities within the application, ADR can identify specific anomalies such as unauthorized access attempts, unexpected spikes in database queries, or sudden changes in API request patterns.
For instance, assume that your e-commerce application experiences an unusual burst of checkout requests within seconds. In this case, ADR tools can correlate that anomalous behavior pattern with known attack signatures like card-testing attacks.
ADR also detects unexpected changes in user behavior, such as a user trying to access features they don’t have access to. These discoveries are notified via data-backed notifications, enabling security teams to investigate potential threats or compromised accounts before any significant damage occurs.
Collecting threat intelligence
Collecting threat intelligence is a vital function of application detection and response systems. ADR generates a comprehensive knowledge base to mitigate application attacks by aggregating data from various sources—such as user behavior, performance fluctuations, application logs, and known threat feeds.
Threat intelligence helps your team understand thetactics, techniques, and procedures (TTPs) employed by threat actors against your application. The ultimate goal is to use that data to address existing flaws and strengthen defenses against future threats. Also, information about attacks enhances incident response decision-making for both human operators and eventual automated systems.
Preventing exploitation of zero-day vulnerabilities
One of the most significant capabilities of ADR is the ability to protect applications fromzero-day vulnerabilities in both custom code and third-party libraries. (Zero-day vulnerabilities are security issues that are not yet known to the public, with no existing patch or fix available.)
Traditional tools like firewalls and antivirus software are ineffective against them because they can only protect against known threats. And these zero-day vulnerabilities are a pressing concern because elite attackers may have access to them—considering they’re sold on the black market.
As we’ve already seen, ADR can detect malicious activity in an application—and that’s true whether it’s based on a known issue or not. While threat analysis on zero-day vulnerabilities won’t be able to provide detailed information, your security team still benefits from the comprehensive context provided by the threat intelligence system. This opens the door to discovering, reporting, and mitigating new, unpatched vulnerabilities before they can be widely exploited.
In particular, ADR solutions utilize behavioral profiling of application components, including open-source libraries. By establishing a baseline of “normal” behavior for each library, ADR can detect any drift or irregularities that may signal a supply chain attack. This proactive approach is key in identifying zero-day vulnerabilities within the open-source ecosystem an application relies on.
ADR vs other detection and response security approaches
What is the difference between ADR and EDR?
EDR (Endpoint Detection and Response) protects end users' endpoint devices—such as laptops, mobile phones, servers, IoT devices, and smartwatches—against threats. EDR monitors devices for malicious activity while ADR protects applications, which may be running on those devices.
What is the difference between ADR and XDR?
While ADR focuses on securing applications at runtime, XDR (Extended Detection and Response) unifies security tools across multiple layers, such as networks, cloud environments, endpoints, and data. ADR collects telemetry specifically from applications, whereas XDR gathers data from all these sources and consolidates it into a centralized hub for comprehensive threat detection and response.
What is the difference between ADR and CDR?
CDR (Cloud Detection and Response) secures cloud environments by detecting and mitigating attacks targeting cloud workloads, services, and platforms. While CDR focuses on protecting the cloud infrastructure, ADR safeguards applications—including applications running within that cloud environment.
Boost your code security with Wiz
As we’ve seen, embracing application detection and response (ADR) is essential for enhancing application security at runtime. While ADR focuses on deployed applications, a thorough security strategy also necessitates proactive measures earlier in the development pipeline. That’s whereWiz Code comes in.
Wiz Code addresses security issues directly within your IDE, pull requests, and CI/CD processes to prevent risks from reaching your cloud environment. Our industry-leading tools offer complete visibility across code and cloud infrastructures, enabling you to identify and remediate vulnerabilities early through automated features like SCA and SBOM, malware detection, secrets scanning, and much more.
Secure coding is the practice of developing software that is resistant to security vulnerabilities by applying security best practices, techniques, and tools early in development.
Secure SDLC (SSDLC) is a framework for enhancing software security by integrating security designs, tools, and processes across the entire development lifecycle.
DAST, or dynamic application security testing, is a testing approach that involves testing an application for different runtime vulnerabilities that come up only when the application is fully functional.
Defense in depth (DiD)—also known as layered defense—is a cybersecurity strategy that aims to safeguard data, networks, systems, and IT assets by using multiple layers of security controls.
IAST (Interactive Application Security Testing) is a security testing method that monitors applications in real-time during runtime to detect vulnerabilities by analyzing code behavior and data flow in live environments.
Open-source software (OSS) software composition analysis (SCA) tools are specialized solutions designed to analyze an application's open-source components and dependencies.