The link between detection and response practices and cloud security has historically been weak. As global organizations increasingly adopt cloud environments, security strategies have largely focused on secure code practices, ensuring proper cloud posture, and fixing misconfigurations. However, this approach has led to an over-reliance on a multitude of detection and response (DR) tools spanning cloud infrastructure, workloads, and even applications. Despite advanced tools, organizations often take weeks or even months to identify and resolve incidents, leaving security teams stretched thin. Many are forced to make hard decisions about which cloud breaches they can realistically defend against due to tool sprawl, soaring cloud security costs, and overwhelming volumes of false positives. By following five targeted steps, security teams can greatly improve their real-time detection and response capabilities for cloud attacks.
Step 1: Implement Real-Time Visibility and Defense
When security teams lack real-time visibility, they’re essentially operating blind and unable to respond effectively to threats. While cloud-native monitoring tools, container security solutions, and endpoint detection and response (EDR) systems offer valuable insights, they tend to focus on specific layers of the environment. A more comprehensive approach is achieved by using eBPF (Extended Berkeley Packet Filter) sensors. eBPF enables deep, real-time observability across the entire stack—network, infrastructure, workloads, and applications—without disrupting production environments. By operating at the kernel level, it delivers visibility without adding performance overhead, making it a powerful solution for runtime security.
Here are some key capabilities to leverage for this step: Topology Graphs provide a visual representation of communication and connectivity among hybrid or multi-cloud assets, allowing teams to understand the interaction across different environments. Comprehensive Asset Insight displays every element in the environment, including clusters, networks, databases, secrets, and operating systems, ensuring no blind spots in the security landscape. External Connectivity Analysis identifies connections to external entities, including details about the country of origin and DNS information, enhancing the ability to spot potential threats coming from outside the organization. Risk Evaluations assess the risk level of each asset, along with its impact on the business, enabling more targeted and effective security measures.
Step 2: Adopt a Multi-Layered Detection Approach
As attackers continue to evolve and evade detection, it becomes increasingly challenging to find and stop breaches before they unfold. The biggest challenge in doing so lies in detecting cloud attack attempts where adversaries are stealthy and exploit multiple attack surfaces—from network exploitation to data injection within a managed service—all while evading detection by cloud detection and response (CDR), cloud workload protection platforms (CWPP), and application detection and response (ADR) solutions. This fragmented strategy has proven inadequate, allowing attackers to exploit gaps between layers to go unnoticed. Monitoring cloud, workloads, and application layers in a single platform provides the widest coverage and protection. It makes it possible to correlate application activity with infrastructure changes in real time, ensuring attacks no longer slip through the cracks.
Here are some key capabilities to leverage for this step: Comprehensive Detection identifies incidents across the cloud, applications, workloads, networks, and APIs, ensuring no threat goes unnoticed. Anomaly Recognition uses machine learning and behavioral analysis to detect deviations that may indicate a threat, adding an intelligent layer of detection that adapts with the evolving behavior of attackers. Identifies Known and Unknown Threats by detecting events according to signatures, Indicators of Compromise (IoCs), Tactics, Techniques, and Procedures (TTPs), and MITRE-defined tactics, this approach covers both new and established threats. Incident Correlation links security events and alerts across different sources to identify patterns and potential threats, enhancing the ability to spot and respond to complex attack vectors.
Step 3: Integrate Vulnerabilities with Incident Data
When vulnerabilities are isolated from incident data, the potential for delayed responses and oversight increases. This is because security teams end up lacking the context they need to understand how vulnerabilities are being exploited or the urgency of patching them in relation to ongoing incidents. In addition, when detection and response efforts leverage runtime monitoring, vulnerability management becomes much more effective by focusing on active and critical risks and reducing noise by more than 90%. This ensures that resources are directed towards addressing the most significant threats, thereby enhancing the overall security posture.
Here are some key capabilities to leverage for this step: Risk Prioritization assesses vulnerabilities according to criteria such as whether they are in application memory, executed, public-facing, exploitable, or fixable, focusing on significant threats that matter most to the organization. Root Cause Analysis identifies the root cause of each vulnerability to address multiple issues at once, ensuring that a single fix can mitigate several vulnerabilities. Verification of Fixes performs ad-hoc scanning of images before deployment to ensure all vulnerabilities have been addressed, integrating security checks into the DevOps pipeline for proactive protection. Compliance Adherence lists all active vulnerabilities as part of a Software Bill of Materials (SBOM) to comply with regulations, ensuring that the organization meets industry standards and legal requirements.
Step 4: Incorporate Identity Details to Understand "Who," "When," and "How"
Threat actors often leverage compromised credentials to execute their attacks, engaging in credential theft, account takeovers, and more. This allows them to masquerade as legitimate users within the environment and go unnoticed for hours or even days. The key to detecting this impersonation is by establishing a baseline for each identity, human or otherwise. Once the typical access pattern of an identity is understood, detecting unusual behavior becomes much easier. Incorporating identity details into security monitoring provides a critical layer of context that is essential for effective threat detection and response.
Here are some key capabilities to leverage for this step: Baseline Monitoring implements tools that capture and analyze baseline behavior for both users and applications, tracking access patterns, resource usage, and interaction with data. Human Identity Security integrates with identity providers for visibility into human identity usage, tracking login times, locations, devices, and behaviors, enabling quick detection of unusual or unauthorized access attempts. Non-Human Identity Security monitors the usage of non-human identities, providing insights into their interactions with cloud resources and highlighting any anomalies that could signal a security threat. Secrets Management identifies every secret in your cloud environment, tracks usage at runtime, and ensures they are securely managed, minimizing the risk of exposure.
Step 5: Establish Diverse Response Actions for Contextual Intervention
As attackers keep evolving, it becomes harder to detect and stop breaches early. A major challenge lies in identifying cloud attacks where adversaries are elusive, using various methods like network exploitation and data injection within managed services. They skillfully evade detection by cloud detection and response (CDR), cloud workload protection platforms (CWPP), and application detection and response (ADR). This fragmented approach allows attackers to slip through the cracks. By monitoring cloud, workloads, and applications on a single platform, you can achieve broader protection. This enables real-time correlation between application activities and infrastructure changes, ensuring attacks are caught promptly.
To bolster this approach, consider these key capabilities: Comprehensive Detection spots incidents across clouds, apps, workloads, networks, and APIs, leaving no threat unnoticed. Anomaly Recognition employs machine learning and behavioral analysis to identify deviations that may signal a threat, adapting intelligently to attackers’ evolving tactics. This system detects both known and unknown threats by using signatures, Indicators of Compromise (IoCs), and MITRE-defined Tactics, Techniques, and Procedures (TTPs). Incident Correlation links security events and alerts from various sources to identify patterns, enhancing the ability to detect and respond to complex attacks.