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Cloud Detection and Response Has Evolving, Cloud-Scale Challenges

Tim Miller

Friday, Apr 26th, 2024

In our previous post, we explored the essentials of cloud detection and response (CDR), highlighting how the cloud's scale and complexity have transformed cybersecurity needs. We discussed the types of threats unique to the cloud and the broad benefits that CDR systems offer to organizations aiming to secure their cloud environments.

This follow-up takes us deeper into some of the challenges that arise with detecting and responding to threats in the cloud. As we consider these challenges, we’ll highlight how CDR, while essential, is just part of what must be a broader, comprehensive cloud-native security strategy. As we do this, we’ll see how the cloud-native application protection platform (CNAPP) plays a role alongside your CDR.

Challenge #1: Multi-cloud diversity brings multi-cloud diversity

The multi-cloud strategy—where an organization diversifies its cloud services across multiple providers—is quickly becoming the norm rather than the exception. One article from Forbes predicts the number of large organizations with a multi-cloud strategy will increase from 76% to 85% in 2024. The shift makes sense; you can pick and choose the best parts of each cloud platform to optimize performance, reduce costs, and enhance resilience.

However, along with strategic diversity, multi-cloud simultaneously introduces a significant layer of complexity, particularly in securing these disparate environments.

Every cloud provider has its own way of doing things

Each cloud provider offers a distinct set of tools, services, and security protocols. For example, the way AWS handles permissions and access control through IAM roles and policies differs from Azure’s approach with Entra ID and role-based access control. When you want to maintain a consistent and cohesive security posture across your clouds, this disparity will complicate your efforts.

The inconsistencies between cloud providers extend to telemetry data as well. For example, a security team must find ways to correlate AWS CloudTrail logs—which monitor CLI, SDK, and API calls within the AWS environment—with Microsoft Azure's Activity Logs—which track resource usage and operations. This integration is crucial for a unified view of security-related data, allowing for effective detection and response to threats.

Vulnerabilities are unique to each cloud

Finally, it’s important to acknowledge that each cloud platform is susceptible to unique vulnerabilities and attack vectors. For example, container security may vary significantly between AWS and GCP, requiring distinct security measures and monitoring strategies to protect containerized applications for each provider. This diversity underscores the necessity for a CDR solution that can be tailored to the specific requirements and security models of each cloud provider.

Solution: Integrate security tools designed for multi-cloud.

Addressing the multi-cloud challenge requires strategic foresight and expertise. Organizations must adopt a flexible security framework capable of integrating different security tools and processes found in a CNAPP, with capabilities such as cloud security posture management (CSPM) and the cloud workload protection platform (CWPP). These tools can enhance a CDR strategy by providing the visibility and control needed across multi-cloud environments.

Challenge #2: Cloud dynamism complicates threat detection

The scalability of cloud environments is a double-edged sword. On one hand, it helps your business grow and adapt with unprecedented flexibility. The rapid deployment of resources can quickly meet your evolving demands. On the other hand, the ability to spin up new cloud resources quickly and conveniently broadens the potential attack surface and substantially complicates the task of threat detection.

Traditional approaches to threat detection look for anomalies from a baseline of normal activity. However, dynamic cloud environments bring significant complications:

Increased noise: The scale of dynamic cloud environments brings with a surge in normal, legitimate traffic to an application. This surge in volume allows anomalous activity to hide within the “noise.”
Evolving baselines: Constantly changing cloud workloads make it difficult to establish and maintain accurate activity baselines, reducing the effectiveness of anomaly detection.

To address these issues and move toward real-time threat analysis, modern enterprises need advanced approaches that can adapt quickly to dynamic conditions. These approaches need to leverage AI/ML with sophisticated pattern recognition, continuously learning from evolving data in order to accurately identify threats amidst ongoing changes.

Threat hunting is not a one-size-fits-all

Traditional baselining methods in threat detection operate under the assumption that all anomalous behavior is suspicious. However, cloud applications increasingly adopt diverse architectures, making this approach less effective. Because application structures and components can vary so widely in the cloud, DevSecOps teams need a more nuanced method of identifying suspicious behavior across the entire application ecosystem.

As applications grow and incorporate more components, the complexity of interactions between these components also increases. This complexity makes rudimentary anomaly-based detection inadequate, as it overlooks the need to understand anomalous behavior connected across multiple components.

Ultimately, effective threat detection in the cloud requires more sophisticated modeling techniques that leverage modern, best-in-class AI/ML technology.

Solution: Couple CDR with AI-powered analysis for comprehensive visibility.

Given this challenge, an effective CDR system must not only scale with the growth of the cloud environment but also adapt to its changing nature. A CDR system must work alongside an advanced security solution, leveraging AI/ML to sift through and analyze this wealth of security data. These systems must be capable of identifying and responding to threats in real time, ensuring that the organization’s security posture keeps pace with its growth.

Challenge #3: AI threats require real-time, continuous monitoring

Proactive, posture-based security solutions often rely on periodic scans (daily or hourly) to identify threats. However, in a dynamic and ever-changing cloud environment, this approach falls short. Modern cyber threats are sophisticated. You can’t detect them by looking at one strand of data or data from one snapshot in time. It’s no surprise that IBM’s Cost of a Data Breach Report 2023 notes that it takes organizations an average of 204 days to identify a breach.

AI-based cyber threats are sophisticated, subtle, and unrelenting

Modern enterprises aren’t the only ones leveraging AI for innovation. Modern cyber attackers are doing the same. They are crafting more adaptive and complex threats, and they’re attacking with unprecedented speed.

AI-assisted malware can conceal its intent until it reaches a specific target (Example: DeepLocker, developed as a proof of concept by IBM Research in 2018).
AI generates noisy traffic patterns to obfuscate intrusion efforts and avoid detection.
AI-backed tools are expediting the discovery of vulnerabilities (Example: Instagram attacks in 2019).

These instances showcase the advanced nature of threats in the digital age, where AI no longer only aids in defense but also enhances the offense. Organizations in the cloud need more agile and informed defenses, capable of understanding and responding to threats in real time.

Solution: Leverage CNAPP alongside CDR.

The effective counter to these advanced threats combines CDR with the CNAPP. CNAPP provides comprehensive visibility across cloud environments, enriched with context and correlation specific to cloud activities. Meanwhile, CDR extends capabilities to include AI-powered threat intelligence and predictive threat analysis, integrating cloud security data with wider system protections. Together, these systems offer a robust defense against the sophisticated, AI-driven threats of today, ensuring real-time detection and rapid response.

Tackling CDR Challenges with CNAPP

In this post, we’ve explored some of the major challenges facing CDR:

Multi-cloud environment diversity
Cloud scalability and dynamism
Limited visibility of an organization’s overall security posture
The fast-paced nature of AI-backed threats

While CDR is an effective and necessary component of cloud security, the full extent of its effectiveness emerges when it’s integrated within a broader, holistic security solution. This is where a CNAPP platform can offer a comprehensive security framework that elevates CDR capabilities.

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