January 13, 2025

Agent vs. Agentless Cloud Security: Why Deployment Methods Matter

Cloud security solutions can be deployed with agentless or agent-based approaches or use a combination of methods. Organizations must weigh which method applies best to the assets and data the tool will protect.

Written by

Kellie Regan

Director, Product Marketing - Cloud Security

Inside the SOC

Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.

Written by

Kellie Regan

Director, Product Marketing - Cloud Security

Jan 2025

The rapid adoption of cloud technologies has brought significant security challenges for organizations of all sizes. According to recent studies, over 70% of enterprises now operate in hybrid or multi-cloud environments, with 93% employing a multi-cloud strategy^[1]. This complexity requires robust security tools, but opinions vary on the best deployment method—agent-based, agentless, or a combination of both.

Agent-based and agentless cloud security approaches offer distinct benefits and limitations, and organizations often make deployment choices based on their unique needs depending on the function of the specific assets covered, the types of data stored, and cloud architecture, such as hybrid or multi-cloud deployments.

For example, agentless solutions are increasingly favored for their ease of deployment and ability to provide broad visibility across dynamic cloud environments. These are especially useful for DevOps teams, with 64% of organizations citing faster deployment as a key reason for adopting agentless tools^[2].

On the other hand, agent-based solutions remain the preferred choice for environments requiring deep monitoring and granular control, such as securing sensitive high-value workloads in industries like finance and healthcare. In fact, over 50% of enterprises with critical infrastructure report relying on agent-based solutions for their advanced protection capabilities^[3].

As the debate continues, many organizations are turning to combined approaches, leveraging the strengths of both agent-based and agentless tools to address the full spectrum of their security needs for comprehensive coverage. Understanding the capabilities and limitations of these methods is critical to building an effective cloud security strategy that adapts to evolving threats and complex infrastructures.

Agent-based cloud security

Agent-based security solutions involve deploying software agents on each device or system that needs protection. Agent-based solutions are great choices when you need in-depth monitoring and protection capabilities. They are ideal for organizations that require deep security controls and real-time active response, particularly in hybrid and on-premises environments.

Key advantages include:

1. Real-time monitoring and protection: Agents detect and block threats like malware, ransomware, and anomalous behaviors in real time, providing ongoing protection and enforcing compliance by continuously monitoring workload activities. Agents enable full control over workloads for active response such as blocking IP addresses, killing processes, disabling accounts, and isolating infected systems from the network, stopping lateral movement.

2. Deep visibility for hybrid environments: Agent-based approaches allow for full visibility across on-premises, hybrid, and multi-cloud environments by deploying agents on physical and virtual machines. Agents offer detailed insights into system behavior, including processes, files, memory, network connections, and more, detecting subtle anomalies that might indicate security threats. Host-based monitoring tracks vulnerabilities at the system and application level, including unpatched software, rogue processes, and unauthorized network activity.

3. Comprehensive coverage: Agents are very effective in hybrid environments (cloud and on-premises), as they can be installed on both physical and virtual machines. Agents can function independently on each host device onto which they are installed, which is especially helpful for endpoints that may operate outside of constant network connectivity.

Challenges:

1. Resource-intensive: Agents can consume CPU, memory, and network resources, which may affect performance, especially in environments with large numbers of workloads or ephemeral resources.

2. Challenging in dynamic environments: Managing hundreds or thousands of agents in highly dynamic or ephemeral environments (e.g., containers, serverless functions) can be complex and labor-intensive.

3. Slower deployment: Requires agent installation on each workload or instance, which can be time-consuming, particularly in large or complex environments.

Agentless cloud security

Agentless security does not require software agents to be installed on each device. Instead, it uses cloud infrastructure and APIs to perform security checks. Agentless solutions are highly scalable with minimal impact on performance, and ideal for cloud-native and highly dynamic environments like serverless and containerized. These solutions are great choices for your cloud-native and multi-cloud environments where rapid deployment, scalability, and minimal impact on performance are critical, but response actions can be handled through external tools or manual processes.

Key advantages include:

1. Scalability and ease of deployment: Because agentless security doesn’t require installation on each individual device, it is much easier to deploy and can quickly scale across a vast number of cloud assets. This approach is ideal for environments where resources are frequently created and destroyed (e.g., serverless, containerized workloads), as there is no need for agent installation or maintenance.

2. Reduced system overhead: Without the need to run local agents, agentless security minimizes the impact on system performance. This is crucial in high-performance environments.

3. Broad visibility: Agentless security connects via API to cloud service providers, offering near-instant visibility and threat detection. It provides a comprehensive view of your cloud environment, making it easier to manage and secure large and complex infrastructures.

Challenges

1. Infrastructure-level monitoring: Agentless solutions rely on cloud service provider logs and API calls, meaning that detection might not be as immediate as agent-based solutions. They collect configuration data and logs, focusing on infrastructure misconfigurations, identity risks, exposed resources, and network traffic, but lack visibility and access to detailed, system-level information such as running processes and host-level vulnerabilities.

2. Cloud-focused: Primarily for cloud environments, although some tools may integrate with on-premises systems through API-based data gathering. For organizations with hybrid cloud environments, this approach fragments visibility and security, leading to blind spots and increasing security risk.

3. Passive remediation: Typically provides alerts and recommendations, but lacks deep control over workloads, requiring manual intervention or orchestration tools (e.g., SOAR platforms) to execute responses. Some agentless tools trigger automated responses via cloud provider APIs (e.g., revoking permissions, adjusting security groups), but with limited scope.

Combined agent-based and agentless approaches

A combined approach leverages the strengths of both agent-based and agentless security for complete coverage. This hybrid strategy helps security teams achieve comprehensive coverage by:

Using agent-based solutions for deep, real-time protection and detailed monitoring of critical systems or sensitive workloads.
Employing agentless solutions for fast deployment, broader visibility, and easier scalability across all cloud assets, which is particularly useful in dynamic cloud environments where workloads frequently change.

The combined approach has distinct practical applications. For example, imagine a financial services company that deals with sensitive transactions. Its security team might use agent-based security for critical databases to ensure stringent protections are in place. Meanwhile, agentless solutions could be ideal for less critical, transient workloads in the cloud, where rapid scalability and minimal performance impact are priorities. With different data types and infrastructures, the combined approach is best.

Best of both worlds: The benefits of a combined approach

The combined approach not only maximizes security efficacy but also aligns with diverse operational needs. This means that all parts of the cloud environment are secured according to their risk profile and functional requirements. Agent-based deployment provides in-depth monitoring and active protection against threats, suitable for environments requiring tight security controls, such as financial services or healthcare data processing systems. Agentless deployment complements agents by offering broader visibility and easier scalability across diverse and dynamic cloud environments, ideal for rapidly changing cloud resources.

There are three major benefits from combining agent-based and agentless approaches.

1. Building a holistic security posture: By integrating both agent-based and agentless technologies, organizations can ensure that all parts of their cloud environments are covered—from persistent, high-risk endpoints to transient cloud resources. This comprehensive coverage is crucial for detecting and responding to threats promptly and effectively.

2. Reducing overhead while boosting scalability: Agentless systems require no software installation on each device, reducing overhead and eliminating the need to update and maintain agents on a large number of endpoints. This makes it easier to scale security as the organization grows or as the cloud environment changes.

3. Applying targeted protection where needed: Agent-based solutions can be deployed on selected assets that handle sensitive information or are critical to business operations, thus providing focused protection without incurring the costs and complexity of universal deployment.

Use cases for a combined approach

A combined approach gives security teams the flexibility to deploy agent-based and agentless solutions based on the specific security requirements of different assets and environments. As a result, organizations can optimize their security expenditures and operational efforts, allowing for greater adaptability in cloud security use cases.

Let’s take a look at how this could practically play out. In the combined approach, agent-based security can perform the following:

1. Deep monitoring and real-time protection:

Workload threat detection: Agent-based solutions monitor individual workloads for suspicious activity, such as unauthorized file changes or unusual resource usage, providing high granularity for detecting threats within critical cloud applications.
Behavioral analysis of applications: By deploying agents on virtual machines or containers, organizations can monitor behavior patterns and flag anomalies indicative of insider threats, lateral movement, or Advanced Persistent Threats (APTs).
Protecting high-sensitivity environments: Agents provide continuous monitoring and advanced threat protection for environments processing sensitive data, such as payment processing systems or healthcare records, leveraging capabilities like memory protection and file integrity monitoring.

2. Cloud asset protection:

Securing critical infrastructure: Agent-based deployments are ideal for assets like databases or storage systems that require real-time defense against exploits and ransomware.
Advanced packet inspection: For high-value assets, agents offer deep packet inspection and in-depth logging to detect stealthy attacks such as data exfiltration.
Customizable threat response: Agents allow for tailored security rules and automated responses at the workload level, such as shutting down compromised instances or quarantining infected files.

At the same time, agentless cloud security provides complementary benefits such as:

1. Broad visibility and compliance:‍

Asset discovery and management: Agentless systems can quickly scan the entire cloud environment to identify and inventory all assets, a crucial capability for maintaining compliance with regulations like GDPR or HIPAA, which require up-to-date records of data locations and usage.
Regulatory compliance auditing and configuration management: Quickly identify gaps in compliance frameworks like PCI DSS or SOC 2 by scanning configurations, permissions, and audit trails without installing agents. Using APIs to check configurations across cloud services ensures that all instances comply with organizational and regulatory standards, an essential aspect for maintaining security hygiene and compliance.
Shadow IT Detection: Detect and map unauthorized cloud services or assets that are spun up without security oversight, ensuring full inventory coverage.

2. Rapid environmental assessment:

Vulnerability assessment of new deployments: In environments where new code is frequently deployed, agentless security can quickly assess new instances, containers, or workloads in CI/CD pipelines for vulnerabilities and misconfigurations, enabling secure deployments at DevOps speed.
Misconfiguration alerts: Detect and alert on common cloud configuration issues, such as exposed storage buckets or overly permissive IAM roles, across cloud providers like AWS, Azure, and GCP.
Policy enforcement: Validate that new resources adhere to established security baselines and organizational policies, preventing security drift during rapid cloud scaling.

Combining agent-based and agentless approaches in cloud security not only maximizes the protective capabilities, but also offers flexibility, efficiency, and comprehensive coverage tailored to the diverse and evolving needs of modern cloud environments. This integrated strategy ensures that organizations can protect their assets more effectively while also adapting quickly to new threats and regulatory requirements.

Darktrace offers complementary and flexible deployment options for holistic cloud security

Powered by multilayered AI, Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that is agentless by default, with optional lightweight, host-based server agents for enhanced real-time actioning and deep inspection. As such, it can deploy in cloud environments in minutes and provide unified visibility and security across hybrid, multi-cloud environments.

With any deployment method, Darktrace supports multi-tenant, hybrid, and serverless cloud environments. Its Self-Learning AI learns the normal behavior across architectures, assets, and users to identify unusual activity that may indicate a threat. With this approach, Darktrace / CLOUD quickly disarms threats, whether they are known, unknown, or completely novel. It then accelerates the investigation process and responds to threats at machine speed.

Learn more about how Darktrace / CLOUD secures multi and hybrid cloud environments in the Solution Brief.

References:

1. Flexera 2023 State of the Cloud Report

2. ESG Research 2023 Report on Cloud-Native Security

3. Gartner, Market Guide for Cloud Workload Protection Platforms, 2023

Written by

Kellie Regan

Director, Product Marketing - Cloud Security

Inside the SOC

Written by

Kellie Regan

Director, Product Marketing - Cloud Security

•

April 17, 2025

Jeffrey Macre

Industrial Security Solutions Architect

•

April 16, 2025

Margaret Cunningham, PhD

Director, Security & AI Strategy, Field CISO

Watch the NIS2 Webinar

Blog

April 24, 2025

The Importance of NDR in Resilient XDR

As threat actors become more adept at targeting and disabling EDR agents, relying solely on endpoint detection leaves critical blind spots.

Network detection and response (NDR) offers the visibility and resilience needed to catch what EDR can’t especially in environments with unmanaged devices or advanced threats that evade local controls.

This blog explores how threat actors can disable or bypass EDR-based XDR solutions and demonstrates how Darktrace’s approach to NDR closes the resulting security gaps with Self-Learning AI that enables autonomous, real-time detection and response.

Threat actors see local security agents as targets

Recent research by security firms has highlighted ‘EDR killers’: tools that deliberately target EDR agents to disable or damage them. These include the known malicious tool EDRKillShifter, the open source EDRSilencer, EDRSandblast and variants of Terminator, and even the legitimate business application HRSword.

The attack surface of any endpoint agent is inevitably large, whether the software is challenged directly, by contesting its local visibility and access mechanisms, or by targeting the Operating System it relies upon. Additionally, threat actors can readily access and analyze EDR tools, and due to their uniformity across environments an exploit proven in a lab setting will likely succeed elsewhere.

Sophos have performed deep research into the EDRShiftKiller tool, which ESET have separately shown became accessible to multiple threat actor groups. Cisco Talos have reported via TheRegister observing significant success rates when an EDR kill was attempted by ransomware actors.

With the local EDR agent silently disabled or evaded, how will the threat be discovered?

What are the limitations of relying solely on EDR?

Cyber attackers will inevitably break through boundary defences, through innovation or trickery or exploiting zero-days. Preventive measures can reduce but not completely stop this. The attackers will always then want to expand beyond their initial access point to achieve persistence and discover and reach high value targets within the business. This is the primary domain of network activity monitoring and NDR, which includes responsibility for securing the many devices that cannot run endpoint agents.

In the insights from a CISA Red Team assessment of a US CNI organization, the Red Team was able to maintain access over the course of months and achieve their target outcomes. The top lesson learned in the report was:

“The assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections.”

This proves that partial, isolated viewpoints are not sufficient to track and analyze what is fundamentally a connected problem – and without the added visibility and detection capabilities of NDR, any downstream SIEM or MDR services also still have nothing to work with.

Why is network detection & response (NDR) critical?

An effective NDR finds threats that disable or can’t be seen by local security agents and generally operates out-of-band, acquiring data from infrastructure such as traffic mirroring from physical or virtual switches. This means that the security system is extremely inaccessible to a threat actor at any stage.

An advanced NDR such as Darktrace / NETWORK is fully capable of detecting even high-end novel and unknown threats.

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

On January 9th 2025, two new vulnerabilities were disclosed in Ivanti Connect Secure and Policy Secure appliances that were under malicious exploitation. Perimeter devices, like Ivanti VPNs, are designed to keep threat actors out of a network, so it's quite serious when these devices are vulnerable.

An NDR solution is critical because it provides network-wide visibility for detecting lateral movement and threats that an EDR might miss, such as identifying command and control sessions (C2) and data exfiltration, even when hidden within encrypted traffic and which an EDR alone may not detect.

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024 – 11 days before the public disclosure of the vulnerability, this early detection highlights the benefits of an anomaly-based network detection method.

Throughout the campaign and based on the network telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated.

Darktrace / NETWORK’s autonomous response capabilities played a critical role in containment by autonomously blocking suspicious connections and enforcing normal behavior patterns. At the same time, Darktrace Cyber AI Analyst™ automatically investigated and correlated the anomalous activity into cohesive incidents, revealing the full scope of the compromise.

This case highlights the importance of real-time, AI-driven network monitoring to detect and disrupt stealthy post-exploitation techniques targeting unmanaged or unprotected systems.

Unlocking adaptive protection for evolving cyber risks

Darktrace / NETWORK uses unique AI engines that learn what is normal behavior for an organization’s entire network, continuously analyzing, mapping and modeling every connection to create a full picture of your devices, identities, connections, and potential attack paths.

With its ability to uncover previously unknown threats as well as detect known threats using signatures and threat intelligence, Darktrace is an essential layer of the security stack. Darktrace has helped secure customers against attacks including 2024 threat actor campaigns against Fortinet’s FortiManager , Palo Alto firewall devices, and more.

Stay tuned for part II of this series which dives deeper into the differences between NDR types.

‍

Credit to Nathaniel Jones VP, Security & AI Strategy, FCISO & Ashanka Iddya, Senior Director of Product Marketing for their contribution to this blog.

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

Blog

April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

man looking at multiple computer screens

Out of all the services honeypotted by Darktrace, Docker is the most commonly attacked, with new strains of malware emerging daily. This blog will analyze a novel malware campaign with a unique obfuscation technique and a new cryptojacking technique.

What is obfuscation?

Obfuscation is a common technique employed by threat actors to prevent signature-based detection of their code, and to make analysis more difficult. This novel campaign uses an interesting technique of obfuscating its payload.

Docker image analysis

The attack begins with a request to launch a container from Docker Hub, specifically the kazutod/tene:ten image. Using Docker Hub’s layer viewer, an analyst can quickly identify what the container is designed to do. In this case, the container is designed to run the ten.py script which is built into itself.

Docker Hub Image Layers, referencing the script ten.py. — Figure 1: Docker Hub Image Layers, referencing the script *ten.py*.

To gain more information on the Python file, Docker’s built in tooling can be used to download the image (docker pull kazutod/tene:ten) and then save it into a format that is easier to work with (docker image save kazutod/tene:ten -o tene.tar). It can then be extracted as a regular tar file for further investigation.

Figure 2: Extraction of the resulting tar file.

The Docker image uses the OCI format, which is a little different to a regular file system. Instead of having a static folder of files, the image consists of layers. Indeed, when running the file command over the sha256 directory, each layer is shown as a tar file, along with a JSON metadata file.

Figure 3: Output of the file command over the sha256 directory.

As the detailed layers are not necessary for analysis, a single command can be used to extract all of them into a single directory, recreating what the container file system would look like:

find blobs/sha256 -type f -exec sh -c 'file "{}" | grep -q "tar archive" && tar -xf "{}" -C root_dir' \;

Figure 4: Result of running the command above.

The find command can then be used to quickly locate where the ten.py script is.

find root_dir -name ten.py

root_dir/app/ten.py

Details of the above ten.py script. — Figure 5: Details of the above *ten.py* script.

This may look complicated at first glance, however after breaking it down, it is fairly simple. The script defines a lambda function (effectively a variable that contains executable code) and runs zlib decompress on the output of base64 decode, which is run on the reversed input. The script then runs the lambda function with an input of the base64 string, and then passes it to exec, which runs the decoded string as Python code.

To help illustrate this, the code can be cleaned up to this simplified function:

def decode(input):
reversed = input[::-1]

decoded = base64.decode(reversed)
decompressed = zlib.decompress(decoded)
return decompressed

decoded_string = decode(the_big_text_blob)
exec(decoded_string) # run the decoded string

This can then be set up as a recipe in Cyberchef, an online tool for data manipulation, to decode it.

Use of Cyberchef to decode the ten.py script. — Figure 6: Use of Cyberchef to decode the *ten.py* script.

The decoded payload calls the decode function again and puts the output into exec. Copy and pasting the new payload into the input shows that it does this another time. Instead of copy-pasting the output into the input all day, a quick script can be used to decode this.

The script below uses the decode function from earlier in order to decode the base64 data and then uses some simple string manipulation to get to the next payload. The script will run this over and over until something interesting happens.

# Decode the initial base64

decoded = decode(initial)
# Remove the first 11 characters and last 3

# so we just have the next base64 string

clamped = decoded[11:-3]

for i in range(1, 100):
# Decode the new payload

decoded = decode(clamped)
# Print it with the current step so we

# can see what’s going on

print(f"Step {i}")

print(decoded)
# Fetch the next base64 string from the

# output, so the next loop iteration will

# decode it

clamped = decoded[11:-3]

Figure 7: Result of the 63rd iteration of this script.

After 63 iterations, the script returns actual code, accompanied by an error from the decode function as a stopping condition was never defined. It not clear what the attacker’s motive to perform so many layers of obfuscation was, as one round of obfuscation versus several likely would not make any meaningful difference to bypassing signature analysis. It’s possible this is an attempt to stop analysts or other hackers from reverse engineering the code. However, it took a matter of minutes to thwart their efforts.

Cryptojacking 2.0?

Figure 8: Cleaned up version of the de-obfuscated code.

The cleaned up code indicates that the malware attempts to set up a connection to teneo[.]pro, which appears to belong to a Web3 startup company.

Teneo appears to be a legitimate company, with Crunchbase reporting that they have raised USD 3 million as part of their seed round [1]. Their service allows users to join a decentralized network, to “make sure their data benefits you” [2]. Practically, their node functions as a distributed social media scraper. In exchange for doing so, users are rewarded with “Teneo Points”, which are a private crypto token.

The malware script simply connects to the websocket and sends keep-alive pings in order to gain more points from Teneo and does not do any actual scraping. Based on the website, most of the rewards are gated behind the number of heartbeats performed, which is likely why this works [2].

Checking out the attacker’s dockerhub profile, this sort of attack seems to be their modus operandi. The most recent container runs an instance of the nexus network client, which is a project to perform distributed zero-knowledge compute tasks in exchange for cryptocurrency.

Typically, traditional cryptojacking attacks rely on using XMRig to directly mine cryptocurrency, however as XMRig is highly detected, attackers are shifting to alternative methods of generating crypto. Whether this is more profitable remains to be seen. There is not currently an easy way to determine the earnings of the attackers due to the more “closed” nature of the private tokens. Translating a user ID to a wallet address does not appear to be possible, and there is limited public information about the tokens themselves. For example, the Teneo token is listed as “preview only” on CoinGecko, with no price information available.

Conclusion

This blog explores an example of Python obfuscation and how to unravel it. Obfuscation remains a ubiquitous technique employed by the majority of malware to aid in detection/defense evasion and being able to de-obfuscate code is an important skill for analysts to possess.

We have also seen this new avenue of cryptominers being deployed, demonstrating that attackers’ techniques are still evolving - even tried and tested fields. The illegitimate use of legitimate tools to obtain rewards is an increasingly common vector. For example, as has been previously documented, 9hits has been used maliciously to earn rewards for the attack in a similar fashion.

Docker remains a highly targeted service, and system administrators need to take steps to ensure it is secure. In general, Docker should never be exposed to the wider internet unless absolutely necessary, and if it is necessary both authentication and firewalling should be employed to ensure only authorized users are able to access the service. Attacks happen every minute, and even leaving the service open for a short period of time may result in a serious compromise.

References

1. https://www.crunchbase.com/funding_round/teneo-protocol-seed--a8ff2ad4

2. https://teneo.pro/

About the author

Nate Bill

Threat Researcher

Your data. Our AI.

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Check out this article by Darktrace: Agent vs. Agentless Cloud Security: Why Deployment Methods Matter

Agent vs. Agentless Cloud Security: Why Deployment Methods Matter

Agent-based cloud security

Agentless cloud security

Combined agent-based and agentless approaches

Best of both worlds: The benefits of a combined approach

Use cases for a combined approach

Why Asset Visibility and Signature-Based Threat Detection Fall Short in ICS Security

Introducing Version 2 of Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2)

Enjoying the blog?

More in this series

Cloud Security Evolution: Why Security Teams are Taking the Lead

Fusing Vulnerability and Threat Data: Enhancing the Depth of Attack Analysis

From Containment to Remediation: Darktrace / CLOUD & Cado Reducing MTTR

Blog

April 24, 2025

The Importance of NDR in Resilient XDR

Threat actors see local security agents as targets

What are the limitations of relying solely on EDR?

Why is network detection & response (NDR) critical?

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

Unlocking adaptive protection for evolving cyber risks

Blog

April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

What is obfuscation?

Docker image analysis

Cryptojacking 2.0?

Conclusion

References