Agent vs Agentless Security

In 2024, Crowdstrike’s faulty agent update drew attention to the risks associated with deploying and updating agents across enterprise environments. But agent-based systems producing valuable alerts correctly can also fail when those alerts are deprioritized by teams who don’t act on them in time (like the 2013 Target breach). And agentless systems come with their own drawbacks — for instance, unmanaged devices, like IoT, often serve as common points of ingress into enterprise systems. So should you rely on agent vs agentless security? Or both?

We answer primary questions like: What’s the difference? But we’ll also go deeper, looking at integration with existing infrastructure, real-time monitoring and response issues, scalability, maintenance, and compliance. 

Oh, and what’s a sensor?

It’s all here, so let’s get clear on what all the options mean for a spectrum of use cases.

What is Agent Security? What is Agentless Security?

Agent security involves installing lightweight software on endpoints, from servers to virtual machines (VMs), containers, laptops, or POS terminals. The agent runs continuously in the background and has direct access to the system’s processes and memory, so it can monitor system behavior and threats, including automatically enforcing policies, isolating devices, or stopping processes.

Agentless security gathers data remotely through cloud provider APIs, network protocols like SSH for Linux boxes, and often through storage buckets and backup systems without installing anything on a target device. Agentless scanning tools can enforce some policies: they can disable misconfigured resources, revoke risky IAM roles, or trigger auto-remediation via a Lambda or function. But it won’t have automatic enforcement inside endpoints, like VMs or containers.

What’s the bottom line? Agents have been the traditional tool to reach inside the system for process-level visibility. Agentless monitoring offers broad visibility with low deployment friction, especially across hybrid and multi-cloud ecosystems.

What is a Sensor in Agent Security?

A sensor is an agent of the future. It does the job of an agent, but it’s different enough to warrant a new name. Sensors are lighter weight and lower impact, without the traditional effects on the environment for which legacy agents were notorious.

Today, with more specialized, less invasive technology, sensors don’t need to use fragile and resource-intensive sidecars that require changes to pod definitions and manifests. And they won’t require root privileges or burdensome CPU and memory overhead. 

Finally, sensors are centered on observability so teams see what’s happening without injecting into every process —  eBPF sensors, for instance, hook into kernel-level and cloud-native telemetry pipelines, making them more passive and hence, safe to run at scale in production.

Sensor vs. Agent	Real-World Results
Next generation sensors vs. legacy agents	Less friction, more observability-focused than legacy technology
No sidecars, no kernel extensions	No need to rearchitect K8s workloads, low touch, safe for production
Passive observability without interference	Deep runtime context without enforcing controls
Minimal CPU and low-permissions install	Safe to deploy at scale, low CPU use, no root required
Equal or better visibility	Modern methods like eBPF offer deeper insight without the blast radius

Today, sensors see more than traditional agents, but without the baggage, meaning there’s no true use case for legacy agents.

How to Choose: Sensor vs. Agentless

Both sensor-based and agentless security can be lifesavers. 

In Capital One’s 2019 breach, a misconfigured AWS WAF exposed S3 data, and agentless scanning could have caught the issue pre-breach. In Uber’s 2022 PowerShell breach, runtime telemetry from a sensor could have caught the issue. 

Sometimes, teams are besieged by a misconfiguration missed by static tools. Other times, it’s lateral movement or exploitation that only a runtime sensor could uncover. Regardless, teams can’t afford to choose nothing. But which approach they choose comes down to what kind of visibility they need.

So let’s explore the use cases for each.

When Sensor-Based Security is Best

Modern sensors are built for deep runtime observability. These tools commonly include:

• Cloud-Native Application Protection Platforms (CNAPPs): While many CNAPPs start agentless, deeper runtime visibility, especially in containers and ephemeral workloads, often requires a lightweight sensor or agent to be deployed.
• Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR): These platforms rely on endpoint agents to track process behavior, detect threats in real time, and respond automatically.
• Container and Kubernetes Security Platforms: Use eBPF-based sensors or sidecars to observe system calls, network traffic, and process activity inside pods without altering workloads.
• Host-Based Intrusion Detection Systems (HIDS): These tools use agents on individual servers or VMs.
• Network Detection and Response (NDR): NDR solutions deploy passive or inline sensors on the network itself to monitor traffic, spot lateral movement, and identify anomalies.
• Some compliance platforms: They use agents to track changes and generate evidence of control enforcement.
• Many Zero Trust / Device Trust Platforms: Many include agents to verify device health, check for the presence of EDR, or enforce security posture.

These solutions excel when teams need:

• Runtime Threat Detection: Sensors catch real-time behaviors like process injection, suspicious child processes, or unusual memory access. Whether Log4Shell exploitation, lateral movement in cloud workloads, or runtime payload delivery, sensors can help.

• Lateral Movement & Privilege Escalation

Only sensors can detect intra-host lateral movement or escalation attempts inside a VM or container. That’s what the 2013 Target breach missed once attackers were inside.

• Fileless Malware / Living-off-the-Land (LotL) Attacks: Sometimes, attacks come with no file and no signature, just strange behavior. Sensors catch it by monitoring behavioral anomalies in systems at runtime. That’s like the PowerShell abuse in Uber’s 2022 breach.
• Policy Enforcement and Runtime Guardrails: Teams that need to block suspicious outbound traffic or catch something strange as it happens? Tools like eBPF sensors make it easy to enforce runtime policies without breaking systems. Thanks to their built-in safety checks and hardening, teams get deep control without the usual risk of kernel crashes.
• Container and Kubernetes Security: eBPF sensors track syscalls, network connections, and file access inside containers without sidecars or node agents. That’s perfect for ephemeral workloads that spin up and vanish in seconds.

Agentless Use Cases (Cloud-Native Visibility)

While sensor-based security shines in a universe where runtime and workload protection and visibility reign, agentless security can still be a logical choice when breadth and speed are more important than depth. Agentless tools will be able to connect via APIs to provide configuration-level visibility and posture assessments across cloud environments.

Tools without sensors or agents include:

• Some Cloud-Native Application Protection Platforms (CNAPPs): Starting agentless (or without a sensor) isn’t uncommon. It eases deployment and offers initial visibility.
• Cloud Security Posture Management (CSPM): Scanning cloud configurations via AWS/GCP/Azure APIs
• Agentless vulnerability scanners: Snapshotting disks or querying cloud services to assess risk
• Cloud IAM Analyzers: Mapping permission graphs and role misuse
• Zero Trust Network Access (ZTNA): Authenticating users and devices without installing clients
• Security Information and Event Management (SIEM) log collectors: Pulling logs via integration instead of endpoint installations

They’re good when teams need:

• Asset Inventory and Cloud Posture Management (CSPM)

Agentless tools quickly discover everything deployed across accounts, regions, and services. For example, they could have identified the Capital One misconfiguration that exposed S3 via a poorly configured WAF.

• IAM Role Misuse and Risk Analysis

Agentless tools audit who can access what, identifying toxic combinations, and flagging excessive privilege escalation paths.

• Compliance Reporting (SOC 2, HIPAA, PCI)

Agentless tools pull cloud provider config and log data to verify encryption, MFA, public access restrictions, etc.

• Quick Deployment in Read-Only Environments

Agentless tools give teams visibility where they can’t or don’t want to deploy anything, like in third-party accounts or unmanaged assets.

• Early-Stage Cloud Security

Agentless tools can handle initial cloud discovery and posture hardening when teams are just getting started.

Sensor vs. Agentless: What to Do

However, it’s challenging to know exactly when (or even whether) the team needs certain capabilities, like quick deployment or protection from certain types of attacks. Here are more concrete needs and how each tool works to add a layer of protection to get the job done.

Goal	Sensor-Based	Agentless
Spot live attacks	Sees syscalls, processes, and users. Can catch threats as they happen.	Sees config state only, not runtime.
Secure fast-changing workloads	Tracks containers and ephemeral compute in real time.	Misses short-lived or fast-moving assets.
Prove controls work	Confirms blocks, alerts, and enforcement at runtime.	Shows configs exist, not if they trigger.
Investigate a breach	Rebuilds timeline: what ran, where, and under whom.	Can’t see inside hosts or link actions to identities.
Audit real identity use	Links users to actions inside hosts or containers.	Maps permissions but not usage.
Map cloud assets fast	Assumes known workloads. Not built for discovery.	Instant inventory via API with zero install.
Meet real compliance needs	Adds runtime evidence to support policies.	Meets config checklist, not behavior proof.
Catch stealthy attacks	Flags PowerShell, curl abuse, in-memory malware.	Misses anything not logged or written to disk.
Stay safe in production	Uses low CPU, no kernel modules, eBPF-hardened.	Zero workload impact, but limited depth.

Agent vs Agentless Security: When to Use Both

Most mature security programs don’t choose sensors or agentless. Instead, they use both strategically. And because sensors and agents bring depth, while agentless tools offer breadth, the two often work better together. 

• Accidentally leave an S3 bucket public? Agentless tools catch the misconfiguration quickly through cloud API scans. Sensors confirm whether the connected workload attempted access to data or ran commands.
• Experience an attacker gaining access via a stolen cloud credential? Agentless visibility maps what the IAM role can do. Sensors show what the attacker did while inside, like process spawns or lateral movement.
• See a workload behaving oddly in Kubernetes? Agentless scans can show no apparent misconfigurations. Sensors can reveal runtime anomalies like unexpected network calls.
• Looking to solidify a breach timeline? Agentless logs show who accessed services and when. Sensors reconstruct the command chain, file access, and process behavior.
• Facing compliance audits? Agentless tools provide config evidence like encryption, multi-factor authentication, and enabled backups. Sensors add proof that controls are enforced live, like a blocked outbound connection or a tampered file.

What About Alternatives?

Sensor-based (or legacy agent) tools vs. agentless tools aren’t the only solutions. There are kernel extensions. There are also tooling options like network monitoring and log-based analysis. So what about those? They do fill some visibility gaps, but they also come with tradeoffs that matter when response time is key. Let’s break it down.

• Network Detection and Response (NDR) can discover lateral movement between systems, but not inside them.

It can spot things like east-west traffic anomalies, suspicious connections between VPCs and hosts, and data exfiltration patterns. But it can’t see inside a host or container, attribute actions to users or processes, or catch attacks that don’t touch the network at all, like local privilege escalation.

• SIEMs are great for analyzing past logs, but they rely on what was recorded. They aggregate logs from across the environment and can coordinate suspicious activities across systems. 

That’s helpful for long-term compliance, alert triage, and centralized event visibility. It’s not ideal for all cases, though. SIEMs depend on what was logged, where logging was enabled, and that attackers did not bypass or disable logs. Without that all-important log, SIEM systems won’t catch an issue.

• EDR and XDR Platforms: These endpoint-focused tools work well when investigating laptop and server threats. They can detect known malicious behavior patterns and respond to user-driven incidents. They struggle with ephemeral cloud workloads and observing container-level system behavior. They may also struggle in Linux/K8s environments. They focus on the machines, while sensors focus on workloads.

Ultimately, there are other tools in the stack. But each comes with its own focus, and none replace the need for smart and purpose-built observability. 

Upwind Delivers Clarity with Agentless + Sensor-Based Observability

This combination of real-time monitoring from the Upwind sensor, paired with comprehensive scanning from Upwind’s Agentless Cloud Scanners, ensures that all aspects of customers’ infrastructure are fully discovered and protected – and they can be up and running in minutes, no matter the complexity of their environment. This powerful combination of runtime-powered insights and lightweight scanning provides customers with instant visibility and deep, integrated protection across hosts, containers, serverless functions, PaaS containers, and classic VM-based environments.

To see our lightweight, production-safe sensor in action, schedule a demo.

FAQ

Do sensors fit into existing detection and response workflows (like SIEM, SOAR, and EDR)?

Yes, modern sensors like those in CNAPP platforms are meant to complement existing detection, enriching alerts on platforms like SIEM, SOAR, and EDR. How? 

• SIEM: Sensors feed deep syscall/process data to be turned into correlated alerts for clearer incident timelines.
• SOAR: Sensors trigger automated playbooks based on live behavior.
• EDR/XDR: Sensors add container and cloud workload visibility where endpoint agents can’t.

Can sensors help reduce false positives, or will they add to alert fatigue?

Sensors can help reduce false positives. They offer deeper, contextual runtime data that can help distinguish real threats from noise. Instead of sending alerts on all misconfigurations, for example, sensors can offer what’s actually happening in workloads so critical issues can be fixed first, for real-world results rather than endless configuration remediation. Sensors offer:

• Behavioral insight: Machine learning can help detect process anomalies as they happen.
• Fewer context-free alerts: Command lineage or syscall patterns make for better accuracy.
• Prioritization: Sensor data means alerts are only triggered when a misconfiguration is actively exploited or behavior deviates.
• Integration: Sensors enrich existing alerts from SIEM or agentless tools with runtime validation.

Will deploying a sensor in production break things?

Sensors shouldn’t break things — at least not if they’re built right. Sensors designed for production from the start come with safety built in. Look for:

• No kernel modules, reducing the risk of crashes and system instabilities.
• Low overhead: Sensors like eBPF should use as little as 1% of CPU with no interference in workload execution.
• A non-intrusive install: Can you integrate your CNAPP, then turn on the sensor? Or does it require sidecars, root access, or application changes?
• Bounded execution by design, where an eBPF verifier ensures programs run safely.

Can sensors support segmentation or policy enforcement in real time?

Modern sensors can support real-time enforcement, especially if they’re coupled with other systems like a CNAPP, SOAR platform, firewall, or K8s admission controller that use its signals to take action.

The sensor itself doesn’t enforce; it observes. But it can trigger enforcement in real-time through integrations, and help these systems recognize the need to suppress an attack it couldn’t otherwise identify.