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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
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13
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
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

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June 1, 2026

Defend What You Trust: Stories from the Front Lines of Modern Cyber Defense

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Modern attacks don’t always announce themselves, follow obvious patterns, or rely on known malware. Often, they move quietly inside trusted systems, authenticated sessions, and everyday behavior.

They don’t break in. They blend in.

That’s why an AI-powered defense is essential. It turns invisible signals into actionable insights at a scale neither analysts nor traditional tools can achieve alone.

Confidence is creating risk

One of the most dangerous assumptions in cybersecurity today is that strong controls equal strong protection.

Multi-factor authentication (MFA), for example, is widely viewed as a foundational safeguard. But as the CISO for a professional sports organization explains, that confidence can be misplaced. “A lot of organizations assume that once you have MFA, those accounts are safe. That’s not true.”

In one instance, his team identified a sophisticated attack where a threat actor bypassed MFA entirely, not by breaking it, but by going around it. A user’s authenticated session was hijacked and re-used, allowing the attacker to impersonate them without triggering traditional controls.

“Darktrace picked up that a session had been re-injected by the hacker, and we were able to block it right away,” he explains.

Attackers anticipate what we miss

Even well-trained users can become entry points.

“An email bypassed our existing security tools,” shares the VP of IT at a U.S.-based risk management services provider.  “The user missed one signal and entered their credentials into a malicious site. That’s what the bad guys count on.”

The organization responded quickly, but not before damage was done. Crucially, this occurred while Darktrace was in “watch mode,” before autonomous response was fully enabled. “Darktrace would have seen that and shut it down immediately,” he notes.

Mistakes and oversights like misconfigurations, forgotten machines, and missed patches can create serious vulnerabilities.

The CIO of a utility services organization shares an instance when Darktrace detected a breach to a client’s network via their ZTNA VPN due to misconfigured MFA. “Darktrace alerted us and autonomously blocked the scanning, preventing what could have been a ransomware-type incident.”  

The most dangerous threats are already inside

The Head of Security at a global business services provider knows firsthand how blind spots can persist inside environments. His team uncovered evidence of dormant ransomware artifacts sitting unnoticed within a company’s environment ¬¬– long before modern detection was in place.

“During a routine file transfer, Darktrace flagged the suspicious activity, identified the ransomware, and immediately quarantined the server,” he recalls.  While the attack was never executed, the implication was significant: the risk existed long before it was finally detected.

Cyber threats are also successful because they take advantage of normal human behavior, exploiting moments of cognitive overload, urgency, and trust.

The Executive Director of IT and Business Applications at a pharmaceutical lab describes the time Darktrace flagged an employee logging into Microsoft 365 from Singapore, despite him being physically located in the U.S. Darktrace immediately cut off his access and within minutes revealed that the employee’s son was using a VPN to play a video game.

While the threat was benign, it demonstrated the strength of AI to use contextual information to detect threats other tools miss. The information also saved security analysts hours of investigation and minimized downtime for the employee. “That level of precision and speed isn’t just convenient, it’s game changing.”

“Unusual” behavior is the new red flag

Detecting modern threats requires an understanding of what “normal” looks like and recognizing when something subtly deviates.

One security leader  at an AI technology enterprise described a scenario in which an employee connected to a proxy service in China. The service itself was legitimate, and although traditional tools didn’t flag it, the behavior was unusual for that user specifically.

“That’s what Darktrace picked up on. The activity turned out to be benign, but without visibility into behavioral deviations, it could just as easily have been something more serious.”

AI shifts defense from reaction to anticipation

These stories point to a fundamental shift by cyber attackers, both tactically and strategically. Because traditional security tools were built to detect what’s already known, modern attacks are often:

  • Credential-based, not malware-based
  • Behavioral, not signature-based
  • Subtle, not overt

They may operate within the boundaries of what appears normal, exploiting what organizations trust, not what they block:

  • Trusted sessions
  • Legitimate services
  • Human error

This is where AI is changing the equation. Rather than relying on predefined rules or known threat signatures, AI can:

  • Establish a baseline of normal behavior
  • Detect subtle anomalies in real time
  • Act autonomously to contain potential threats

Resilience, not perfection, is the new security standard

As these frontline experiences show, the organizations that lead are those that move beyond reactive defense and embrace AI as a core part of their strategy.

It eliminates the blind spots and uncertainty, says the CISO of a professional sports organization. “If you lack visibility, you’re not managing risk, you’re assuming it. AI gives you the actionable insights needed to turn uncertainty into control.”

And it provides the speed and agility that are vital when seconds matter, says the Executive Director of IT and Business Applications. “When Darktrace alerted us at 3:00 am to a ransomware attack, it had already quarantined the affected systems, blocked the attacker’s access, and provided us with the critical details and time needed to investigate. That action likely saved us hundreds of thousands, if not millions, of dollars.”

The modern SOC has become a cornerstone of enterprise resilience, responsible for protecting data and operational continuity while enabling digital growth and innovation. For today’s security professional, that means success is no longer measured by what they keep out, but by what they protect: revenue, reputation, and trust.

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

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How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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About the author
Oakley Cox
Director of Product
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