What is Cloud Application Detection and Response (CADR)?

The shift to cloud-native architecture is driving scalability, flexibility, and cost improvements organizations couldn’t achieve with on-prem monoliths. However, it’s also increasing complexity and creating new attack surfaces. As organizations attempt to address these new threats with individual security and monitoring solutions, tool sprawl can quickly lead to fragmented visibility and a lack of contextual understanding in the SOC. 

Cloud application detection and response (CADR) is a security approach that can help organizations address this problem with contextual and behavioral detection capabilities that provide a comprehensive view of threats across cloud infrastructure, Kubernetes clusters, and deployed applications. CADR gathers and aggregates data from operating system events, containers, workloads, APIs, and applications to provide end-to-end visibility. It leverages advanced sensors to provide comprehensive visibility with real-time threat detection and response. 

This article discusses the core concepts of CADR and explains how CADR enhances threat detection.

The business problem CADR solves

CADR addresses the security and visibility challenges that come with a shift to modern technologies like microservices and cloud-first strategies. 

The complexity of cloud environments has made traditional security tools ineffective. These tools were designed for static, on-premises environments, and cannot keep pace with the rapidly changing nature of infrastructures. Moreover, cloud environments pose unique detection and response challenges such as large volumes of logs/events to sift through, the volume of new application deployments and changes, and a constantly evolving attack surface.

Let’s explore why that is a fundamental shift from the problems traditional security approaches are intended to address. 

Limitations of traditional security approaches

Traditional security solutions are designed for monolithic applications and are suited to static environments with clearly defined system boundaries. These solutions struggle to detect and respond to security vulnerabilities in real time, as they lack end-to-end context about the cloud infrastructure and deployed applications. Some of the challenges with traditional security approaches are:

• Tool sprawl: Tool sprawl refers to the use of multiple tools for security that work in isolation, leading to fragmented visibility as they fail to provide contextual insights to security teams. These tools struggle to correlate data across systems and may have different alerting mechanisms, making it difficult to identify suspicious patterns or respond to incidents swiftly.
• Limitations of legacy detection methods: Legacy detection tools often use a rule-based approach to identify security threats, failing to handle the scale and complexity of cloud environments. This approach struggles to detect sophisticated attacks that exploit zero-day vulnerabilities, credential stuffing, or supply chain attacks.

Core concepts of behavioral CADR

With CADR, organizations can consolidate tooling, increase visibility, and add context to their threat detection and response capabilities to address the shortcomings of traditional security approaches. Let’s explore the details that enable CADR to achieve these outcomes.  

CADR components

CADR gathers logs and metrics from multiple sources to get a holistic picture of the environment. A robust CADR solution should include the following three components. 

Cloud management logs

These logs capture detailed information about all the actions performed in a cloud environment across compute, storage, and networking resources. This includes events for resource creation, modification, deletion, and changes in access permissions, allowing security teams to identify unusual actions and correlate them with security incidents. For example, AWS CloudTrail is a service used for operational auditing, security risk management, and compliance by recording every action taken by a user or role within the AWS cloud environment. 

Container workload logs

Container logs are critical for identifying suspicious activities such as unauthorized network communication, excessive privileges, or data transfers. Technologies like the Extended Berkeley Packet Filter (eBPF) allow for the isolated execution of custom programs within the Linux operating system, enabling the collection of detailed kernel observability, tracing, and profiling of containerized environments. 

Application layer logs

Application layer logs are crucial for identifying suspicious activities at runtime within the deployed applications. The CADR system analyzes logs generated from network requests, responses, application errors, and user activity to detect threats like account takeovers, business logic attacks, and unauthorized data access attempts. 

Tools like OpenTelemetry enable the instrumentation, collection, and export of logs, helping organizations analyze software behavior and detect security issues.

The mechanisms of CADR

CADR helps organizations enhance their threat detection capabilities, reduce false positives, and automate their response mechanisms with robust detection capabilities and incident response strategies. 

Multilayered detection capabilities

CADR uses a multilayered detection strategy by integrating across different levels, including cloud infrastructure, applications, and user behavior, enabling it to gain end-to-end visibility of the system. It analyzes the collected logs and metrics using advanced threat intelligence techniques, including anomaly detection and machine learning, to identify issues in real time, allowing the detection of complex and evolving security threats.

Additionally, CADR uses behavioral profiling to establish a baseline of normal user activity through continuous monitoring. This approach reduces the number of false positives, allowing security teams to focus on actionable threats. 

Incident response strategies

In a distributed cloud environment, manual incident response is inefficient in identifying, containing, and mitigating security threats due to the complexity and scale of cloud environments. CADR provides automatic response mechanisms that can enable certain actions to minimize the impact of identified incidents. For example, it can automatically block access and stop affected applications to isolate the incident without human intervention. CADR also offers a soft quarantine option for the production environment that isolates the compromised processes or containers from other resources to prevent contamination and reduce the scale of the incident. This allows the rest of the environment to function while the root cause of the incident is identified and fixed.

How CrowdStrike can help you address complex cloud security threats

Cloud environments are susceptible to a wide variety of security threats due to their large attack surface, scale, and complexity. Organizations can’t rely on traditional security measures but instead must integrate tools like behavioral CADR solutions, which collect data from multiple cloud sources, analyze user activity, and use advanced threat detection techniques to identify and respond to threats in real time. 

CrowdStrike Falcon^Ⓡ is an AI-native cybersecurity platform that provides comprehensive threat detection and incident response for cloud environments. With CrowdStrike, organizations benefit from capabilities such as:

• Advanced cloud detection and response (CDR) to deliver 89% faster detection and response times
• Next-Gen SIEM (NG SIEM) to unify security information, reduce logging costs, increase visibility for the SOC, and stop breaches faster

Additionally, with CrowdStrike managed services, our team of experts become your team’s cybersecurity partners, supercharging your ability to prepare for, respond to, and recover from security incidents. 

To discover how CrowdStrike can enhance your security posture, sign up for a free trial today.