What is malware scanning?
Malware scanning is the process of inspecting files, systems, and cloud resources for signs of malicious software—before it causes damage. In today’s fast-moving cloud environments, malware scanning must go beyond legacy antivirus and adapt to modern threats like fileless malware, supply chain compromise, and runtime-based attacks.
In this article, we’ll break down how malware scanning works, the types of threats it catches, and how detection and response must evolve for cloud-native systems.
Malware comes in many forms—each with its own behaviors and detection challenges. Some of the most common types include:
Viruses infect healthy files to alter or erase data, propagating by duplicating themselves across computers and connected networks.
Worms self-replicate across networks without human interaction.
Trojan horses masquerade as legitimate software while creating backdoors.
Ransomware demands payment for the release of encrypted data.
Spyware & keyloggers secretly gather user information.
Botnets, composed of networks of infected devices, can coordinate attacks, e.g., distributed denial of service (DDoS).
Since the dawn of computing, malware has fueled a relentless tug-of-war between cyber attackers and those working to stop them.
Traditional antivirus tools rely on signature-based scanning, comparing files and processes against a database of known malware. While effective against known threats, these tools struggle to detect modern malware variants—especially in dynamic, cloud-native environments where workloads are short-lived and distributed.
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How Malware Scanning Works: Detection, Analysis, and Response
Malware scanning generally follows three core phases to uncover, examine, and mitigate software threats.
1. Detection
Malware detection techniques typically fall into two categories:
Signature-based detection, which compares files to a database of known malware patterns—fast, but blind to novel threats.
Behavior-based detection, which monitors how programs behave in real time, flagging suspicious actions like code injection, unusual encryption, or process spawning.
In practice, most security teams use a hybrid approach to catch both known and emerging threats. Behavioral analysis is especially important in cloud environments where malware may not leave behind static artifacts. Advanced techniques—such as tracing system calls or monitoring process behavior at runtime—are critical for identifying fileless threats that operate only in memory.
2. Analysis
Once malware is detected, security tools often perform:
Static analysis: Examining code without running it.
Dynamic analysis: Executing the file in a sandbox to observe real behavior.
In modern environments, response actions can be automated—isolating affected workloads, revoking access, or triggering playbooks as soon as malware is detected.
3. Remediation
Once confirmed, malware is quarantined or deleted. Alerts are triggered, and in cloud-native setups, automated workflows can kick in to isolate workloads or roll back changes. Wiz, for example, can automatically generate remediation tickets with misconfiguration context and playbooks.
The changing malware landscape in the cloud
Traditional malware scanning tools weren’t built for the complexity and speed of cloud-native infrastructure. Here’s what’s changed—and why detection strategies need to evolve.
Wider attack surfaces
The rapid adoption of containers, ephemeral virtual machines, and serverless functions has drastically expanded an enterprise’s potential attack surface. These short-lived assets are created and destroyed quickly, offering little time for traditional detection tools to respond. A malicious container can easily run and terminate within minutes, undetected.
These short-lived assets often disappear before traditional scanners can detect threats—especially if scanning relies on scheduled scans or agents.
IaC misconfigurations and third-party risks
Another issue is that modern development relies heavily oninfrastructure as code (IaC) and complex software supply chains. Misconfigured IaC templates can introduce vulnerabilities, while third-party dependencies might carry embedded malware. And it takes just one compromised package to expose thousands of your downstream apps.
Multi-cloud complexities
Multi-cloud environments further complicate detection. Each cloud provider offers different security and logging mechanisms, fragmenting visibility. Attackers can exploit this fragmentation, breaching a vulnerable asset in one cloud and moving laterally into more sensitive systems elsewhere.
A real-world example of malware is the SeleniumGreed attack. A popular test automation framework, Selenium has no default authentication mechanism for its Grid server. Many users deploy it as-is, exposing it to the internet. Attackers were able to exploit this oversight, hijacking these servers to install crypto miners.
Effective malware scanning needs to normalize signals across environments and retain context even when workloads are ephemeral.
Limitations of traditional malware scanning
Traditional tools were built for static infrastructure—long-lived servers and fixed endpoints. When it comes to detecting modern threats in cloud-native environments, these guys simply don’t make the cut:
Malicious base images: Endpoint tools may detect compromised container-based images after deployment, but these images can then infect every downstream workload; this is why you need to detect them during build time.
CI/CD pipeline risks: Attackers target CI/CD pipelines by injecting malicious code into popular libraries. But by the time even behavior-based runtime scanning flags this, the threat can easily have spread.
Cloud object storage: Endpoint scanners rarely monitor cloud object storage continuously; a malware-laced file can sit in an S3 bucket for hours before a scheduled scan or manual mount occurs.
Advanced fileless threats: Modern malware often uses in-memory execution or legitimate system tools, requiring behavioral analysis and runtime visibility; these threats leave no static artifacts for traditional signature scanners to catch.
Legacy tools also struggle to detect fileless malware and in-memory execution techniques, which often require visibility into runtime behaviors like system calls, process trees, and unexpected network activity.
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Modern malware scanning for cloud-native environments
Addressing today’s threats requires more than signature scans. Security teams need continuous, context-aware malware detection that works across ephemeral cloud workloads and runtime environments.
Agentless asset discovery
Modern platforms scan virtual machines, containers, functions, and storage without deploying agents. This gives you full coverage of short-lived assets. Take a file uploaded to S3. You want to be able to scan that in real time before it reaches other systems.
Advanced runtime behavioral detection
Behavioral scanning at runtime is critical for spotting advanced threats—like cryptominers, memory-resident malware, or abuse of legitimate tools (LOLBins). Modern techniques use technologies like eBPF to monitor low-level system activity (e.g., process launches, file writes, socket creation) in real time—without injecting agents into the kernel.
Code-to-runtime correlation
You need rapid root cause analysis, with answers you can act upon. That means a platform that traces any discovered threat back to the originating container image, Dockerfile, or IaC template. This traceability helps teams rapidly understand the origin of an infection—whether it came from a container image, a CI/CD pipeline artifact, or a misconfigured IaC template.
Contextual threat scoring
Lots of malware alerts may be lighting up your dashboard. But which ones need urgent remediation?
Solutions today need to prioritize threats based on potential impact—factoring in data sensitivity, internet exposure, and privilege levels. For instance, an infected image that can access a production database will be flagged as a higher risk than one in a dev environment.
DevSecOps integration
DevSecOps is here to stay. Making sure security policies can block vulnerable builds from reaching production means embedding scanning into CI/CD pipelines is non-negotiable. Embedding malware scanning into your CI/CD workflows allows teams to block malicious artifacts before deployment—preventing infected builds from ever reaching production.
How Wiz helps with cloud malware scanning
The Wiz Defend platform unifies agentless scanning and runtime behavioral detection to catch malware across the full attack chain, from build pipelines to active workloads. Agentless scanning provides continuous visibility across VMs, containers, serverless functions, cloud object storage, and code repositories, including pattern-based detection using proprietary YARA rules built by the Wiz Research Team to identify emerging malware variants. The Wiz Runtime Sensor, a lightweight eBPF-based component, complements agentless detection by performing real-time behavioral analysis for executed binaries, detecting dropped files, rogue processes, lateral movement, and container escapes that signature-based tools often miss. The Wiz Security Graph then correlates malware findings with cloud-native context like excessive permissions, internet exposure, and access to sensitive data, so teams can prioritize remediation based on real attack paths rather than treating every detection equally.
Wiz AI Agents now accelerate every stage of this workflow. When the sensor or agentless scan flags malware, the Blue Agent automatically investigates the threat, correlating runtime signals, cloud telemetry, and identity context to deliver a verdict with full reasoning and confidence. The Green Agent then traces confirmed malware findings to their root cause, identifies ownership, and generates environment-specific remediation steps, including opening pull requests to fix the issue in code. The Red Agent works proactively, continuously probing web applications and APIs for exploitable weaknesses that could serve as malware delivery vectors. These agents are orchestrated through Wiz Workflows, where teams can define automated containment for high-confidence malware detections while routing lower-confidence findings to analysts for review.
This coverage now extends to AI workloads through the Wiz AI Application Protection Platform (AI-APP), which maps models, agents, tools, and data flows across infrastructure like AWS Bedrock, Azure AI, and Vertex AI. AI-APP surfaces AI-specific risks such as exposed inference endpoints, misconfigured guardrails, and over-permissioned agent services, while Wiz Defend adds runtime detection for threats like prompt injection and data exfiltration through agent tools. Whether the target is a traditional workload or an AI application, Wiz gives security teams a unified, context-rich view of malware risk from code to cloud to runtime.Get a demo to see how Wiz detects and responds to malware across your entire cloud environment.
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