Cloud environments change continuously as workloads scale, identities receive new permissions, applications communicate across services, and development teams release updates. This speed creates a security challenge: identifying risky configurations is important, but enterprises also need to recognize malicious activity while infrastructure is operating.
Threat detection capabilities help security teams monitor runtime behavior, analyze suspicious activity, correlate signals, and investigate incidents across workloads and cloud services. The approaches differ considerably among providers. Some emphasize workload telemetry, while others combine runtime signals with identity, network, vulnerability, or application context. Enterprises should evaluate these differences against their architecture and operational priorities. The following five providers illustrate distinct approaches to cloud security with integrated detection capabilities.
1. Fortinet
Fortinet provides cloud security capabilities designed to protect applications, workloads, infrastructure, and networks across public, private, and hybrid environments. Its approach connects preventive controls with visibility and security operations so organizations can address cloud risks within a broader enterprise security architecture.
Teams considering cloud security solutions with threat detection can assess how workload protection, application security, network controls, and security operations fit their existing cloud architecture.
Threat detection becomes particularly important when workloads operate across environments with different services and operational models. Security teams need enough context to distinguish routine activity from behavior that may indicate compromised credentials, malicious processes, unexpected communication, or exploitation attempts.
Enterprises evaluating this approach should consider workload coverage, supported cloud platforms, telemetry, policy administration, incident investigation, automation, and integration with existing security operations. They should also determine whether cloud events can be correlated with activity elsewhere in the enterprise rather than remaining isolated within individual cloud consoles.
2. Aqua Security
Aqua Security focuses on cloud-native application protection, including containers, Kubernetes, serverless workloads, and other cloud infrastructure. Runtime monitoring is an important part of its approach because activity that occurs inside a running workload may not be visible through configuration assessment or pre-deployment scanning alone.
The company’s broader cloud operations research examines the growing importance of runtime evidence as security teams investigate activity in dynamic cloud environments.
This approach can be relevant to enterprises with extensive cloud-native development and container adoption. Runtime telemetry can provide information about processes, privilege changes, file access, memory activity, and outbound communications, giving analysts additional context after suspicious behavior appears.
Organizations should assess supported workload types, deployment methods, Kubernetes coverage, investigation workflows, forensic visibility, and integrations with development and security operations. The balance between preventive controls and runtime monitoring should also match how applications are built and operated.
3. SentinelOne
SentinelOne addresses cloud workload protection through multiple detection methods designed to identify suspicious or malicious activity affecting running workloads. Its approach combines behavioral analysis with additional detection engines and response capabilities.
A discussion within its wider enterprise security commentary describes a rules-based intelligence engine used alongside other detection methods to identify known malicious activity affecting cloud workloads.
Enterprises considering this model should examine the types of telemetry collected from workloads and how separate signals are combined during investigation. Behavioral detection can be useful when suspicious activity does not correspond neatly to a known signature, while intelligence and rules can help identify previously classified malicious behavior.
Evaluation should include workload support, detection methods, alert context, response options, policy customization, and integration with broader security operations. Organizations with mixed infrastructure should also determine whether investigations can connect cloud workload activity with incidents occurring elsewhere.
4. Sysdig
Sysdig emphasizes runtime visibility across containers, Kubernetes, hosts, cloud services, and other cloud-native infrastructure. Its model uses information about what is actively occurring in production to help teams detect suspicious behavior and determine which risks deserve attention.
Its current cloud infrastructure perspectives describe runtime insights as a way to observe live activity across workloads, identities, and cloud services while supporting detection and investigation.
Runtime context can be especially useful in environments where workloads are short-lived. A container or process may disappear before a delayed assessment occurs, making continuous telemetry valuable for understanding what happened during the workload’s active period.
Enterprises should compare runtime coverage, telemetry depth, Kubernetes visibility, behavioral monitoring, investigation tools, response options, and vulnerability prioritization. They should also consider the operational effect of collecting detailed runtime information and whether alerts can be incorporated efficiently into existing incident-response processes.
5. Upwind
Upwind uses a runtime-focused cloud security model that combines information from workloads, networks, identities, Kubernetes activity, and cloud logs. Its approach is designed to provide security teams with context about active behavior rather than relying solely on static posture information.
The company’s broader cloud industry analysis discusses correlating runtime telemetry with identity information, access logs, and other cloud activity to build a more complete view of security events.
For enterprises, correlation can help reduce the effort required to reconstruct an incident manually from several independent data sources. A suspicious process may become more meaningful when analysts can connect it with unusual identity activity, unexpected network communication, or changes elsewhere in the environment.
Organizations evaluating this model should examine sensor deployment, cloud coverage, runtime visibility, detection logic, investigation workflows, response mechanisms, and integration with existing operational tools. They should also consider whether the approach provides sufficient context without generating an excessive volume of low-priority alerts.
What to Evaluate in Cloud Threat Detection
Effective detection depends on visibility. Cloud environments generate information from workloads, identities, applications, network activity, control planes, and infrastructure services. A provider should help teams turn these signals into useful context rather than simply increasing the number of alerts analysts must review.
Logging is therefore an important evaluation area. Federal logging implementation guidance highlights the role of cloud logs and telemetry in supporting threat hunting and incident-response activities. Enterprises should examine what data a platform collects, how long information remains available, and whether analysts can search historical activity during investigations.
Detection also needs to reflect the speed of modern cloud infrastructure. Static assessments remain useful for finding vulnerabilities and configuration weaknesses, but they cannot always explain what a workload is doing at a particular moment. Runtime monitoring adds behavioral evidence that can help determine whether an exposure is actively involved in suspicious activity.
Market activity also demonstrates the growing importance placed on cloud-wide security visibility. Recent cloud market reporting describes continued investment in platforms intended to address security across cloud environments, including detection and investigation from infrastructure through runtime.
Enterprises should ultimately compare telemetry coverage, behavioral analysis, alert correlation, identity context, workload visibility, investigation depth, automation, and response capabilities. The strongest fit is not necessarily the provider producing the most findings. It is the one that gives security teams enough relevant evidence to recognize suspicious behavior, understand its scope, and respond within the organization’s operational model.
FAQs
What is built-in cloud threat detection?
Built-in cloud threat detection monitors cloud activity for suspicious behavior and provides information that security teams can use to investigate potential attacks or compromised resources.
Why is runtime visibility important for cloud security?
Runtime visibility shows what workloads and services are actually doing while operating, helping teams identify behavior that configuration checks or periodic scans may not reveal.
What should enterprises compare between threat detection providers?
Enterprises should compare telemetry coverage, detection methods, workload support, identity context, alert correlation, investigation capabilities, response options, and integration with existing security operations.
