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May 28, 2019

[Part 2] Top Cyber Hygiene Issues Leading to a Breach

Spotting cyber hygiene issues caused by a lapse of attention requires AI tools that alert critical changes to network activity. Read part two here!
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
Max Heinemeyer
Global Field CISO
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28
May 2019

Read the first part: Part one — A perimeter in ruins

Earlier this month, I discussed some of the most critical challenges that today’s institutions face in their efforts to reinforce the network perimeter. Eliminating common attack vectors, from unauthorized uploads in the cloud to outdated protocol usage on-premise, is an essential step toward a more secure digital future.

Ultimately, however, I concluded that even flawless cyber hygiene at the perimeter will never be a panacea for all possible cyber-threats, since defenders cannot possibly address vulnerabilities about which they aren’t yet aware. Building strong borders is vital, clearly, but as attackers continue to launch novel attacks, even 50-foot walls are imperiled by 50-foot ladders.

Of course, such concerns become merely academic when your walls aren’t placed correctly, or watched attentively, or expanded when the digital estate grows. For countless employees and organizations alike, the allure of convenience has weakened the perimeter in all of these ways and more, rendering the work of cyber-criminals exponentially easier. Yet given the complexity of the modern enterprise, discovering exactly where users have cut corners is often difficult for human security teams alone. Spotting cyber hygiene issues caused by a lack of due diligence — like the five detailed below — therefore requires AI tools that alert on critical changes to network activity in real time.

Issue #6: Not keeping an inventory of hardware on the network

As all manner of non-traditional IT makes its way into workplaces around the world, keeping an inventory of these seamlessly integrated devices often proves an arduous undertaking, one that many organizations shirk altogether. Between app-controlled thermostats and smart refrigerators, connected cameras and Bluetooth sensors, few security teams possess a rigorous list of the hardware under their care.

Yet attaining 100% network visibility is a prerequisite to any viable security posture. Attackers are increasingly targeting poorly secured IoT devices to bypass the perimeter at its weakest points, before moving laterally to compromise more sensitive databases and machines. By analyzing all traffic from the entire enterprise, Darktrace detects when new devices come online and alert on any unusual activity from them with its AI models, some of which are:

  • Device / New Device with Attack Tools
  • Unusual Activity / Anomalous SMB Read & Write from New Device
  • Unusual Activity / Sustained Unusual Activity from New Device
  • Unusual Activity / Unusual Activity from New Device

Issue #7: Using corporate devices for private use

While the divide between corporate and private networks is a primary facet of cyber hygiene, few employees are immune to the temptation and convenience of using company devices for personal use. Whether it’s torrenting movies, visiting social media websites, or checking personal email accounts during the workday, these activities all expose carefully guarded corporate environments to ones that are far less secure. At the same time, many organizations lack visibility over their own online traffic, preventing their security teams from catching such risky behavior until it’s already too late.

Employees have also been known to violate internal compliance policies by downloading unauthorized software for private purposes, which introduces serious security risks and opens the door for supply chain attacks. Darktrace has detected a plethora of threats related to such downloads across our customer base, including outdated software, network scanners, BitTorrent clients, and crypto-mining programs. Such compliance issues trigger a number of Darktrace’s behavioral models, for example:

  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Incoming RAR File
  • Compliance / BitTorrent
  • Compliance / Crypto Currency Mining Activity

To bypass compliance policies and access resources blocked by network administrators, employees often turn to VPNs as well as onion routing services like Tor, which facilitate anonymous communication. These services are equivalent to inhibiting security controls on the offending device; consequently, companies must have the ability to detect and terminate them whenever they are used on the network. Because Darktrace provides 100% visibility across the digital infrastructure, it can flag private VPN and Tor sessions with the following example models:

  • Anomalous Connection / New Outbound VPN
  • Compliance / Privacy VPN
  • Compliance / Tor Usage

Darktrace detected one such case earlier this year wherein a corporate device connected to a third-party VPN. Although this activity is not inherently risky or threatening in all situations, Darktrace’s understanding of the company’s network revealed that the device was the only one using the VPN — strongly suggesting a compliance violation. Moreover, when the device was not using the VPN service, it was seen making a large amount of HTTP post requests to another rare destination and displaying other signs of infection. It turned out that the device was infected with the elusive Ursnif trojan.

Figure 1: Darktrace’s external site summary showing that only one device in the network connected to the VPN.

Issue #8: Lack of strong access management

Ensuring that only rightful users have access to private company resources is a foundational component of cyber security. Yet as these users and their privileges continuously evolve, maintaining strong access management can be time-consuming and difficult.

Out of all the users in the network, the accounts to which the most attention should be paid are those with administrator or root privileges. While it is common to keep a tight control on high-privilege accounts, there are still organizations that find it hard to manage the access control well, making their devices more vulnerable to both malware and insider threats. In fact, even well-intentioned insiders can jeopardize the organization in the absence of strong access management, such as employees who download unauthorized software without understanding its associated risks.

Darktrace has a list of models to detect the unusual usage of credentials, including:

  • User / New Admin Credentials on Client
  • User / Overactive User Credential
  • SaaS / Unusual SaaS Administration

Issue #9: TFTP Usage

Trivial File Transfer Protocol (TFTP) is an application layer protocol commonly employed to transfer files between devices. Due to its relatively simplistic design and easy implementation, TFTP was very popular in the past. In the context of today’s sophisticated cyber-threats, however, TFTP has become highly insecure. Among the protocol’s numerous weaknesses from a cyber hygiene perspective is its lack of authentication mechanisms, a flaw which allows essentially anyone to read and write resources on the exposed device.

Darktrace’s Compliance / External TFTP model enables network administrators to detect any incoming TFTP connections from external IP addresses that don’t normally connect to the network. Crucially, Darktrace AI’s understanding what constitutes “normal” versus “abnormal” for each particular network serves to differentiate the most serious threats, as TFTP connections from a rare IP address are much more likely to be malicious than similar connections between known IP addresses on the network.

TFTP is just one example of insecure protocol usage – Darktrace monitors for the abnormal usage of various other attack-prone protocols as well. Another example is Telnet.

Issue #10: Unencrypted data transferred between internal and external devices

While encrypting communication can be a hassle, cleartext messages are liable to be intercepted or even altered by malicious actors — with potentially devastating ramifications. Indeed, Darktrace’s Compliance / FTP / Unusual Outbound FTP model has frequently flagged credentials being sent via unencrypted channels, which attackers could have used to access privileged resources within the company’s network.

In the first few months of 2019, Darktrace detected an unusual connection made to an external device on port 1414 using the IBM WebSphere MQ Protocol. When potentially sensitive information was transmitted in cleartext, Darktrace AI alerted the customer in real time.

Figure 2: Packet capture showing that potential sensitive information was captured

Sacrificing convenience for security in these most egregious cases remains the foundation of robust cyber hygiene, whether that means not torrenting Shrek 2 on a work laptop or taking inventory of the smart juicer in the office kitchen. Of course, just as no perimeter defenses are formidable enough to keep motivated attackers at bay, so too is there no level of due diligence sufficient to close off all possible attack vectors or ensure that all employees are compliant with internal policies. With cyber AI defenses like Darktrace, security teams have an extra set of eyes watching out for poor cyber hygiene practices across the entire digital infrastructure, empowering them to grow those infrastructures with confidence.

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
Max Heinemeyer
Global Field CISO

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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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