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December 5, 2024

Protecting Your Hybrid Cloud: The Future of Cloud Security in 2025 and Beyond

In the coming years, cloud security will not only need to adapt to increasingly complex environments as ecosystems become more distributed, but also to rapidly evolving threats like supply chain attacks, advanced misconfiguration exploits, and credential theft. AI-powered cloud security tools can help security teams keep up.
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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05
Dec 2024

Cloud security in 2025

The future of cybersecurity is being shaped by the rapid adoption of cloud technologies.

As Gartner reports, “By 2027, more than 70% of enterprises will use industry cloud platforms to accelerate their business initiatives, up from less than 15% in 2023” [1].

As organizations continue to transition workloads and sensitive data to cloud environments, the complexity of securing distributed infrastructures grows. In 2025, cloud security will need to address increasingly sophisticated threats with innovative approaches to ensure resilience and trust.

Emerging threats in cloud security:

  1. Supply chain attacks in the cloud: Threat actors are targeting vulnerabilities in cloud networks, including third-party integrations and APIs. These attacks can have wide-spanning impacts, jeopardizing data security and possibly even compromising multiple organizations at once. As a result, robust detection and response capabilities are essential to identify and neutralize these attacks before they escalate.
  2. Advanced misconfiguration exploits: Misconfigurations remain a leading cause of cloud security breaches. Attackers are exploiting these vulnerabilities across dynamic infrastructures, underscoring the need for tools that provide continuous compliance validation in the future of cloud computing.
  3. Credential theft with evolving Tactics, Techniques, and Procedures (TTPs): While credential theft can result from phishing attacks, it can also happen through other means like malware, lateral movement, data breaches, weak and reused passwords, and social engineering. Adversarial innovation in carrying out these attacks requires security teams to use proactive defense strategies.
  4. Insider threats and privilege misuse: Inadequate monitoring of Identity and Access Management (IAM) in cloud security increases the risk of insider threats. The adoption of zero-trust architectures is key to mitigating these risks.
  5. Threats exploiting dynamic cloud scaling: Attackers take advantage of the dynamic nature of cloud computing, leveraging ephemeral workloads and autoscaling features to evade detection. This makes adaptive and AI-driven detection and response critical because it can more easily parse behavioral data that would take human security teams longer to investigate.

Where the industry is headed

In 2025, cloud infrastructures will become even more distributed and interconnected. Multi-cloud and hybrid models will dominate, so organizations will have to optimize workloads across platforms. At the same time, the growing adoption of edge computing and containerized applications will decentralize operations further. These trends demand security solutions that are agile, unified, and capable of adapting to rapid changes in cloud environments.

Emerging challenges in securing cloud environments

The transition to highly distributed and dynamic cloud ecosystems introduces the following key challenges:

  1. Limited visibility
    As organizations adopt multiple platforms and services, gaining a unified view of cloud architectures becomes increasingly difficult. This lack of visibility makes it unclear where sensitive data resides, which identities can access it and how, and if there are potential vulnerabilities in configurations and API infrastructure. Without end-to-end monitoring, detecting and mitigating threats in real time becomes nearly impossible.
  2. Complex environments
    The blend of public, private, and hybrid clouds, coupled with diverse service types (SaaS, PaaS, IaaS), creates a security landscape rife with configuration challenges. Each layer adds complexity, increasing the risk of misconfigurations, inconsistent policy enforcement, and gaps in defenses – all of which attackers may exploit.
  3. Dynamic nature of cloud
    Cloud infrastructures are designed to scale resources on demand, but this fluidity poses significant challenges to threat detection and incident response. Changes in configurations, ephemeral workloads, and fluctuating access points mean that on-prem network security mindsets cannot be applied to cloud security and many traditional cloud security approaches still fall short in addressing threats in real time.

Looking forward: Protecting the cloud in 2025 and beyond

Addressing these challenges requires innovation in visibility tools, AI-driven threat detection, and policy automation. The future of cloud security hinges on solutions that adapt to complexity and scale, ensuring organizations can securely navigate the growing demands of cloud-first operations.

Unsupervised Machine Learning (ML) enhances cloud security

Unlike supervised ML, which relies on labeled datasets, unsupervised ML identifies patterns and deviations in data without predefined rules, making it particularly effective in dynamic and unpredictable environments like the cloud. By analyzing the baseline behavior in cloud environments, such as typical user activity, network traffic, and resource utilization, unsupervised ML and supporting models can identify behavioral deviations linked to suspicious activity like unusual login times, irregular API calls, or unexpected data transfers, therefore flagging them as potential threats.

Learn more about how multi-layered ML improves real-time cloud detection and response in the data sheet “AI enhances cloud security.

Agent vs. Agentless deployment

The future of cloud security is increasingly focused on combining agent-based and agentless solutions to address the complexities of hybrid and multi-cloud environments.

This integrated approach enables organizations to align security measures with the specific risks and operational needs of their assets, ensuring comprehensive protection.

Agent-based systems provide deep monitoring and active threat mitigation, making them ideal for high-security environments like financial services and healthcare, where compliance and sensitive data require stringent safeguards.

Meanwhile, agentless systems offer broad visibility and scalability, seamlessly covering dynamic cloud resources without the need for extensive deployment efforts.

Together, a combination of these approaches ensures that all parts of the cloud environment are protected according to their unique risk profiles and functional requirements.

The growing adoption of this strategy highlights a shift toward adaptive, scalable, and efficient security solutions, reflecting the priorities of a rapidly evolving cloud landscape.

To learn more about how these technologies are reshaping cloud defenses, read the blog “Agent vs. Agentless Cloud Security: Why Deployment Methods Matter.”

Shifting responsibilities: security teams must get more comfortable with cloud mindsets

Traditionally, many organizations left cloud security to dedicated cloud teams. However, it is becoming more and more common for security teams to take on the responsibilities of securing the cloud. This is also true of organizations undergoing cloud migration and spinning up cloud infrastructure for the first time.

Notably, the usual approaches to other types of cybersecurity can’t be applied the exact same way to the cloud. With the inherent dynamism and flexibility of the cloud, the necessary security mindset differs greatly from those for the network or datacenters, with which security teams may be more familiar.

For example, IAM is both critical and distinct to cloud computing, and the associated policies, rules, and downstream impacts require intentional care. IAM rules not only govern people, but also non-human entities like service accounts, API keys, and OAuth tokens. These considerations are unique to cloud security, and established teams may need to learn new skills to reduce security gaps in the cloud.

Discover more about the teams that impact modern cloud security in the blog "Cloud Security Evolution: Why Security Teams are Taking the Lead."

The importance of visibility: The future of network security in the cloud

As organizations transition to cloud environments, they still have much of their data in on-premises networks, meaning that maintaining visibility across both on-premises and cloud environments is essential for securing critical assets and ensuring seamless operations. Without a unified security strategy, gaps between these infrastructures and the teams which manage them can leave organizations vulnerable to cyber-attacks.

Shared visibility across both on-premises and cloud environments unifies SecOps and DevOps teams, enabling them to generate actionable insights and develop a cohesive approach. This alignment helps confidently mitigate risks across the cloud and network while streamlining workflows and accelerating the cloud migration journey—all without compromising security or operational continuity.

Read more about the importance of end-to-end visibility in the modern threat landscape in the blog "Breaking Silos: Why Unified Security is Critical in Hybrid World."

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Ready to transform your cloud security approach? Download the CISO's Guide to Cloud Security now!

References:

[1] Gartner, June 5, 2024, “The Expanding Enterprise Investment in Cloud Security,” Available at: https://www.gartner.com/en/newsroom/press-releases/2024-06-05-the-expanding-enterprise-investment-in-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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July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

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AI adoption in cybersecurity: Beyond the hype

Security operations today face a paradox. On one hand, artificial intelligence (AI) promises sweeping transformation from automating routine tasks to augmenting threat detection and response. On the other hand, security leaders are under immense pressure to separate meaningful innovation from vendor hype.

To help CISOs and security teams navigate this landscape, we’ve developed the most in-depth and actionable AI Maturity Model in the industry. Built in collaboration with AI and cybersecurity experts, this framework provides a structured path to understanding, measuring, and advancing AI adoption across the security lifecycle.

Overview of AI maturity levels in cybersecurity

Why a maturity model? And why now?

In our conversations and research with security leaders, a recurring theme has emerged:

There’s no shortage of AI solutions, but there is a shortage of clarity and understanding of AI uses cases.

In fact, Gartner estimates that “by 2027, over 40% of Agentic AI projects will be canceled due to escalating costs, unclear business value, or inadequate risk controls. Teams are experimenting, but many aren’t seeing meaningful outcomes. The need for a standardized way to evaluate progress and make informed investments has never been greater.

That’s why we created the AI Security Maturity Model, a strategic framework that:

  • Defines five clear levels of AI maturity, from manual processes (L0) to full AI Delegation (L4)
  • Delineating the outcomes derived between Agentic GenAI and Specialized AI Agent Systems
  • Applies across core functions such as risk management, threat detection, alert triage, and incident response
  • Links AI maturity to real-world outcomes like reduced risk, improved efficiency, and scalable operations

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How is maturity assessed in this model?

The AI Maturity Model for Cybersecurity is grounded in operational insights from nearly 10,000 global deployments of Darktrace's Self-Learning AI and Cyber AI Analyst. Rather than relying on abstract theory or vendor benchmarks, the model reflects what security teams are actually doing, where AI is being adopted, how it's being used, and what outcomes it’s delivering.

This real-world foundation allows the model to offer a practical, experience-based view of AI maturity. It helps teams assess their current state and identify realistic next steps based on how organizations like theirs are evolving.

Why Darktrace?

AI has been central to Darktrace’s mission since its inception in 2013, not just as a feature, but the foundation. With over a decade of experience building and deploying AI in real-world security environments, we’ve learned where it works, where it doesn’t, and how to get the most value from it. This model reflects that insight, helping security leaders find the right path forward for their people, processes, and tools

Security teams today are asking big, important questions:

  • What should we actually use AI for?
  • How are other teams using it — and what’s working?
  • What are vendors offering, and what’s just hype?
  • Will AI ever replace people in the SOC?

These questions are valid, and they’re not always easy to answer. That’s why we created this model: to help security leaders move past buzzwords and build a clear, realistic plan for applying AI across the SOC.

The structure: From experimentation to autonomy

The model outlines five levels of maturity :

L0 – Manual Operations: Processes are mostly manual with limited automation of some tasks.

L1 – Automation Rules: Manually maintained or externally-sourced automation rules and logic are used wherever possible.

L2 – AI Assistance: AI assists research but is not trusted to make good decisions. This includes GenAI agents requiring manual oversight for errors.

L3 – AI Collaboration: Specialized cybersecurity AI agent systems  with business technology context are trusted with specific tasks and decisions. GenAI has limited uses where errors are acceptable.

L4 – AI Delegation: Specialized AI agent systems with far wider business operations and impact context perform most cybersecurity tasks and decisions independently, with only high-level oversight needed.

Each level reflects a shift, not only in technology, but in people and processes. As AI matures, analysts evolve from executors to strategic overseers.

Strategic benefits for security leaders

The maturity model isn’t just about technology adoption it’s about aligning AI investments with measurable operational outcomes. Here’s what it enables:

SOC fatigue is real, and AI can help

Most teams still struggle with alert volume, investigation delays, and reactive processes. AI adoption is inconsistent and often siloed. When integrated well, AI can make a meaningful difference in making security teams more effective

GenAI is error prone, requiring strong human oversight

While there is a lot of hype around GenAI agentic systems, teams will need to account for inaccuracy and hallucination in Agentic GenAI systems.

AI’s real value lies in progression

The biggest gains don’t come from isolated use cases, but from integrating AI across the lifecycle, from preparation through detection to containment and recovery.

Trust and oversight are key initially but evolves in later levels

Early-stage adoption keeps humans fully in control. By L3 and L4, AI systems act independently within defined bounds, freeing humans for strategic oversight.

People’s roles shift meaningfully

As AI matures, analyst roles consolidate and elevate from labor intensive task execution to high-value decision-making, focusing on critical, high business impact activities, improving processes and AI governance.

Outcome, not hype, defines maturity

AI maturity isn’t about tech presence, it’s about measurable impact on risk reduction, response time, and operational resilience.

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Outcomes across the AI Security Maturity Model

The Security Organization experiences an evolution of cybersecurity outcomes as teams progress from manual operations to AI delegation. Each level represents a step-change in efficiency, accuracy, and strategic value.

L0 – Manual Operations

At this stage, analysts manually handle triage, investigation, patching, and reporting manually using basic, non-automated tools. The result is reactive, labor-intensive operations where most alerts go uninvestigated and risk management remains inconsistent.

L1 – Automation Rules

At this stage, analysts manage rule-based automation tools like SOAR and XDR, which offer some efficiency gains but still require constant tuning. Operations remain constrained by human bandwidth and predefined workflows.

L2 – AI Assistance

At this stage, AI assists with research, summarization, and triage, reducing analyst workload but requiring close oversight due to potential errors. Detection improves, but trust in autonomous decision-making remains limited.

L3 – AI Collaboration

At this stage, AI performs full investigations and recommends actions, while analysts focus on high-risk decisions and refining detection strategies. Purpose-built agentic AI systems with business context are trusted with specific tasks, improving precision and prioritization.

L4 – AI Delegation

At this stage, Specialized AI Agent Systems performs most security tasks independently at machine speed, while human teams provide high-level strategic oversight. This means the highest time and effort commitment activities by the human security team is focused on proactive activities while AI handles routine cybersecurity tasks

Specialized AI Agent Systems operate with deep business context including impact context to drive fast, effective decisions.

Join the webinar

Get a look at the minds shaping this model by joining our upcoming webinar using this link. We’ll walk through real use cases, share lessons learned from the field, and show how security teams are navigating the path to operational AI safely, strategically, and successfully.

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About the author
Ashanka Iddya
Senior Director, Product Marketing

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July 17, 2025

Forensics or Fauxrensics: Five Core Capabilities for Cloud Forensics and Incident Response

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The speed and scale at which new cloud resources can be spun up has resulted in uncontrolled deployments, misconfigurations, and security risks. It has had security teams racing to secure their business’ rapid migration from traditional on-premises environments to the cloud.

While many organizations have successfully extended their prevention and detection capabilities to the cloud, they are now experiencing another major gap: forensics and incident response.

Once something bad has been identified, understanding its true scope and impact is nearly impossible at times. The proliferation of cloud resources across a multitude of cloud providers, and the addition of container and serverless capabilities all add to the complexities. It’s clear that organizations need a better way to manage cloud incident response.

Security teams are looking to move past their homegrown solutions and open-source tools to incorporate real cloud forensics capabilities. However, with the increased buzz around cloud forensics, it can be challenging to decipher what is real cloud forensics, and what is “fauxrensics.”

This blog covers the five core capabilities that security teams should consider when evaluating a cloud forensics and incident response solution.

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1. Depth of data

There have been many conversations among the security community about whether cloud forensics is just log analysis. The reality, however, is that cloud forensics necessitates access to a robust dataset that extends far beyond traditional log data sources.

While logs provide valuable insights, a forensics investigation demands a deeper understanding derived from multiple data sources, including disk, network, and memory, within the cloud infrastructure. Full disk analysis complements log analysis, offering crucial context for identifying the root cause and scope of an incident.

For instance, when investigating an incident involving a Kubernetes cluster running on an EC2 instance, access to bash history can provide insights into the commands executed by attackers on the affected instance, which would not be available through cloud logs alone.

Having all of the evidence in one place is also a capability that can significantly streamline investigations, unifying your evidence be it disk images, memory captures or cloud logs, into a single timeline allowing security teams to reconstruct an attacks origin, path and impact far more easily. Multi–cloud environments also require platforms that can support aggregating data from many providers and services into one place. Doing this enables more holistic investigations and reduces security blind spots.

There is also the importance of collecting data from ephemeral resources in modern cloud and containerized environments. Critical evidence can be lost in seconds as resources are constantly spinning up and down, so having the ability to capture this data before its gone can be a huge advantage to security teams, rather than having to figure out what happened after the affected service is long gone.

darktrace / cloud, cado, cloud logs, ost, and memory information. value of cloud combined analysis

2. Chain of custody

Chain of custody is extremely critical in the context of legal proceedings and is an essential component of forensics and incident response. However, chain of custody in the cloud can be extremely complex with the number of people who have access and the rise of multi-cloud environments.

In the cloud, maintaining a reliable chain of custody becomes even more complex than it already is, due to having to account for multiple access points, service providers and third parties. Having automated evidence tracking is a must. It means that all actions are logged, from collection to storage to access. Automation also minimizes the chance of human error, reducing the risk of mistakes or gaps in evidence handling, especially in high pressure fast moving investigations.

The ability to preserve unaltered copies of forensic evidence in a secure manner is required to ensure integrity throughout an investigation. It is not just a technical concern, its a legal one, ensuring that your evidence handling is documented and time stamped allows it to stand up to court or regulatory review.

Real cloud forensics platforms should autonomously handle chain of custody in the background, recording and safeguarding evidence without human intervention.

3. Automated collection and isolation

When malicious activity is detected, the speed at which security teams can determine root cause and scope is essential to reducing Mean Time to Response (MTTR).

Automated forensic data collection and system isolation ensures that evidence is collected and compromised resources are isolated at the first sign of malicious activity. This can often be before an attacker has had the change to move latterly or cover their tracks. This enables security teams to prevent potential damage and spread while a deeper-dive forensics investigation takes place. This method also ensures critical incident evidence residing in ephemeral environments is preserved in the event it is needed for an investigation. This evidence may only exist for minutes, leaving no time for a human analyst to capture it.

Cloud forensics and incident response platforms should offer the ability to natively integrate with incident detection and alerting systems and/or built-in product automation rules to trigger evidence capture and resource isolation.

4. Ease of use

Security teams shouldn’t require deep cloud or incident response knowledge to perform forensic investigations of cloud resources. They already have enough on their plates.

While traditional forensics tools and approaches have made investigation and response extremely tedious and complex, modern forensics platforms prioritize usability at their core, and leverage automation to drastically simplify the end-to-end incident response process, even when an incident spans multiple Cloud Service Providers (CSPs).

Useability is a core requirement for any modern forensics platform. Security teams should not need to have indepth knowledge of every system and resource in a given estate. Workflows, automation and guidance should make it possible for an analyst to investigate whatever resource they need to.

Unifying the workflow across multiple clouds can also save security teams a huge amount of time and resources. Investigations can often span multiple CSP’s. A good security platform should provide a single place to search, correlate and analyze evidence across all environments.

Offering features such as cross cloud support, data enrichment, a single timeline view, saved search, and faceted search can help advanced analysts achieve greater efficiency, and novice analysts are able to participate in more complex investigations.

5. Incident preparedness

Incident response shouldn't just be reactive. Modern security teams need to regularly test their ability to acquire new evidence, triage assets and respond to threats across both new and existing resources, ensuring readiness even in the rapidly changing environments of the cloud.  Having the ability to continuously assess your incident response and forensics workflows enables you to rapidly improve your processes and identify and mitigate any gaps identified that could prevent the organization from being able to effectively respond to potential threats.

Real forensics platforms deliver features that enable security teams to prepare extensively and understand their shortcomings before they are in the heat of an incident. For example, cloud forensics platforms can provide the ability to:

  • Run readiness checks and see readiness trends over time
  • Identify and mitigate issues that could prevent rapid investigation and response
  • Ensure the correct logging, management agents, and other cloud-native tools are appropriately configured and operational
  • Ensure that data gathered during an investigation can be decrypted
  • Verify that permissions are aligned with best practices and are capable of supporting incident response efforts

Cloud forensics with Darktrace

Darktrace delivers a proactive approach to cyber resilience in a single cybersecurity platform, including cloud coverage. Darktrace / CLOUD is a real time Cloud Detection and Response (CDR) solution built with advanced AI to make cloud security accessible to all security teams and SOCs. By using multiple machine learning techniques, Darktrace brings unprecedented visibility, threat detection, investigation, and incident response to hybrid and multi-cloud environments.

Darktrace’s cloud offerings have been bolstered with the acquisition of Cado Security Ltd., which enables security teams to gain immediate access to forensic-level data in multi-cloud, container, serverless, SaaS, and on-premises environments.

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About the author
Calum Hall
Technical Content Researcher
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