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March 29, 2023

Email Security & Future Innovations: Educating Employees

As online attackers change to targeted and sophisticated attacks, Darktrace stresses the importance of protection and utilizing steady verification codes.
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
Dan Fein
VP, Product
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29
Mar 2023

In an escalating threat landscape with email as the primary target, IT teams need to move far beyond traditional methods of email security that haven’t evolved fast enough – they’re trained on historical attack data, so only catch what they’ve seen before. By design, they are permanently playing catch up to continually innovating attackers, taking an average of 13 days to recognize new attacks[1]

Phishing attacks are getting more targeted and sophisticated as attackers innovate in two key areas: delivery tactics, and social engineering. On the malware delivery side, attackers are increasingly ‘piggybacking’ off the legitimate infrastructure and reputations of services like SharePoint and OneDrive, as well as legitimate email accounts, to evade security tools. 

To evade the human on the other end of the email, attackers are tapping into new social engineering tactics, exploiting fear, uncertainty, and doubt (FUD) and evoking a sense of urgency as ever, but now have tools at their disposal to enable tailored and personalized social engineering at scale. 

With the help of tools such as ChatGPT, threat actors can leverage AI technologies to impersonate trusted organizations and contacts – including damaging business email compromises, realistic spear phishing, spoofing, and social engineering. In fact, Darktrace found that the average linguistic complexity of phishing emails has jumped by 17% since the release of ChatGPT.  

This is just one example of accelerating attack sophistication – lowering the barrier to entry and improving outcomes for attackers. It forms part of a wider trend of the attack landscape moving from low-sophistication, low-impact, and generic phishing tactics - a 'spray and pray' approach - to more targeted, sophisticated, and higher impact attacks that fall outside of the typical detection remit for any tool relying on rules and signatures. Generative AI and other technologies in the attackers' toolkit will soon enable the launch of these attacks at scale, and only being able to catch known threats that have been seen before will no longer be enough.

Figure 1: The progression of attacks and relative coverage of email security tools

In an escalating threat landscape with email as the primary target, the vast majority of email security tools haven't evolved fast enough – they’re trained on historical attack data, so only catch what they’ve seen before. They look to the past to try and predict the next attack, and are designed to catch today’s attacks tomorrow.

Organizations are increasingly moving towards AI systems, but not all AI is the same, and the application of that AI is crucial. IT and security teams need to move towards email security that is context-aware and leverages AI for deep behavioral analysis. And it’s a proven approach, successfully catching attacks that slip by other tools across thousands of organizations. And email security today needs to be more about just protecting the inbox. It needs to address not just malicious emails, but the full 360-degree view of a user across their email messages and accounts, as well as extended coverage where email bleeds into collaboration tools/SaaS. For many organizations, the question is not if they should upgrade their email security, but when – how much longer can they risk relying on email security that’s stuck looking to the past?  

The Email Security Industry: Playing Catch-Up

Gateways and ICES (Integrated Cloud Email Security) providers have something in common: they look to past attacks in order to try to predict the future. They often rely on previous threat intelligence and on assembling ‘deny-lists’ of known bad elements of emails already identified as malicious – these tools fail to meet the reality of the contemporary threat landscape. Some of these tools attempt to use AI to improve this flawed approach, looking not only for direct matches, but using "data augmentation" to try and find similar-looking emails. But this approach is still inherently blind to novel threats. 

These tools tend to be resource-intensive, requiring constant policy maintenance combined with the hand-to-hand combat of releasing held-but-legitimate emails and holding back malicious phishing emails. This burden of manually releasing individual emails typically falls on security teams, teams that are frequently small with multiple areas of responsibility. The solution is to deploy technology that autonomously stops the bad while allowing the good through, and adapts to changes in the organization – technology that actually fits the definition of ‘set and forget’.  

Becoming behavioral and context-aware  

There is a seismic shift underway in the industry, from “secure” email gateways to intelligent AI-driven thinking. The right approach is to understand the behaviors of end users – how each person uses their inbox and what constitutes ‘normal’ for each user – in order to detect what’s not normal. It makes use of context – how and when people communicate, and with who – to spot the unusual and to flag to the user when something doesn’t look quite right – and why. Basically, a system that understands you. Not past attacks.  

Darktrace has developed a fundamentally different approach to AI, one that doesn’t learn what’s dangerous from historical data but from a deep continuous understanding of each organization and their users. Only a complex understanding of the normal day-to-day behavior of each employee can accurately determine whether or not an email actually belongs in that recipient’s inbox. 

Whether it’s phishing, ransomware, invoice fraud, executive impersonation, or a novel technique, leveraging AI for behavioral analysis allows for faster decision-making – it doesn’t need to wait for a Patient Zero to contain a new attack because it can stop malicious threats on first encounter. This increased confidence in detection allows for more a precise response – targeted action to remove only the riskiest parts of an email, rather than taking a broad blanket response out of caution – in order to reduce risk with minimal disruption to the business. 

Returning to our attack spectrum, as the attack landscape moves increasingly towards highly sophisticated attacks that use novel or seemingly legitimate infrastructure to deliver malware and induce victims, it has never been more important to detect and issue an appropriate response to these high-impact and targeted attacks. 

Fig 2: How Darktrace combined with native email security to cover the full spectrum of attacks

Understanding you and a 360° view of the end user  

We know that modern email security isn’t limited to the inbox alone – it has to encompass a full understanding of a user’s normal behavior across email and beyond. Traditional email tools are focused solely on inbound email as the point of breach, which fails to protect against the potentially catastrophic damage caused by a successful email attack once an account has been compromised.    

Fig 3: A 360° understanding of a user reveals their digital touchpoints beyond Microsoft

In order to have complete context around what is normal for a user, it’s crucial to understand their activity within Microsoft 365, Google Workspace, Salesforce, Dropbox, and even their device on the network. Monitoring devices (as well as inboxes) for symptoms of infection is crucial to determining whether or not an email has been malicious, and if similar emails need to be withheld in the future. Combining with data from cloud apps enables a more holistic view of identity-based attacks. 

Understanding a user in the context of the whole organization – which also means network, cloud, and endpoint data – brings additional context to light to improve decision making, and connecting email security with external data on the attack surface can help proactively find malicious domains, so that defenses can be hardened before an attack is even launched.

Educating and Engaging Your Employees

Ultimately, it’s employees who interact with any given email. If organizations can successfully empower this user base, they will end up with a smarter workforce, fewer successful attacks, and a security team with more time on their hands for better, strategic work. 

The tools that succeed best will be those that can leverage AI to help employees become more security-conscious. While some emails are evidently malicious and should never enter an employee’s inbox, there is a significant grey area of emails that have potentially risky elements. The majority of security tools will either withhold these emails completely – even though they might be business critical – or let them through scot-free. But what if these grey-area emails could in fact be used as training opportunities?    

As opposed to phishing simulation vendors, behavioral AI can improve security awareness holistically throughout organizations by training users with a light touch via their own inboxes – bringing the end user into the loop to harden defenses.  

The new frontier of email security fights AI with AI, and organizations who lag behind might end up learning the hard way. Read on for our blog series about how these technologies can transform the employee experience, dynamize deployment, augment security teams and form part of an integrated defensive loop.    

[1] 13 days is the mean average of phishing payloads active in the wild between the response of Darktrace/Email compared to the earliest of 16 independent feeds submitted by other email security technologies.

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
Dan Fein
VP, Product

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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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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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