Blog
/
Email
/
December 4, 2024

Phishing Attacks Surge Over 600% in the Buildup to Black Friday

Black Friday and Cyber Monday are prime targets for cyber-attacks, as consumer spending rises and threat actors flock to take advantage. Darktrace analysis reveals a surge in retail cyber scams at the opening of the peak 2024 shopping period, and the top brands that scammers love to impersonate. Plus, don’t forget to check out our top tips for holiday-proofing your SOC before you clock off for the festive season.
No items found.
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.
No items found.
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
04
Dec 2024

Defenders are accustomed now to an uptick in cyber-attacks around the holiday period. The festive shopping season creates ideal conditions for cybercriminals. Consumers are inundated with time-sensitive deals, while retailers handle record-breaking transaction volumes at speed. This environment makes it harder than ever to identify suspicious activity.

An investigation conducted by Darktrace’s global analyst team revealed that Christmas-themed phishing attacks leapt 327%1 around the world and Black Friday and Cyber Monday themed phishing attacks soared to 692% last week compared to the beginning of November2 (4th - 9th November), as threat actors seek to take advantage of the busy holiday shopping period.

The United States retail sector saw the most marked increase in threat actors crafting convincing emails purporting to be from well-known brands, mimicking promotional emails. Attacks designed to look like they came from major brands including Walmart – which was easily the most mimicked US brand – Macy’s, Target, Old Navy, and Best Buy3 increased by more than 2000% during peak shopping periods.

Darktrace analysis also highlighted a redistribution of scammers’ resources to take advantage of the festive shopping season, moving from targeting businesses to consumers. The impersonation of major consumer brands, dominated by Amazon and PayPal4, increased by 92% globally between analyzed periods, while the spoofing of workplace-focused brands, like Adobe, Zoom and LinkedIn, decreased by 9%.

Major retail brands invest heavily in safeguarding themselves and their customers from scams and cyberattacks, particularly during the holiday season. However, phishing and website spoofing occur outside the retailers' legitimate infrastructure and security controls, making it difficult to catch and prevent every instance due to their sheer volume. While advancements like AI are helping security teams narrow the gap, brand impersonation remains a persistent challenge.

Multiple attack methods exploit trust during holiday rush

Darktrace’s findings demonstrate some of the most common brand spoofing strategies used by attackers during the holiday season:

Domain spoofing, which sees attackers create near perfect replicas of retail websites, complete with lookalike domain names and branding, to trick consumers into handing over personal and payment details.  

Brand spoofing, where attackers send a phishing email designed to look like a favorite retailer, enticing their target to click a link for a discount, when in fact the link downloads malware to their device.  

Safelink smuggling, which involves an attacker intentionally getting their malicious payload rewritten by a security solution’s Safelink capability to then propagate the rewritten URL to others. This not only evades detection but also undermines trust in email security tools. Darktrace observed over 300,000 cases of Safelinks being included in unexpected and suspicious contexts over a period of 3 months.

Multi-stage attacks which combine these tactics into a single attack: brand spoofing emails lead unsuspecting shoppers directly to domain spoofed websites that harvest login or payment details, creating a seamless deception that hands personal and financial data directly to attackers. This coordinated approach exploits the chaos of holiday sales, when shoppers are primed to expect high volumes of retail emails and website traffic promoting significant savings.

A spike in cyber-criminal activity which extends beyond email

While email often serves as the front door to an organization and the initial avenue of attack, Darktrace frequently observes a surge in cyber-attacks during public holidays5. These “off-peak” attacks exploit common organizational practices and human vulnerabilities with greater ease.

When staff numbers are reduced, and employees mentally and physically disconnect from work, the speed of detection and response has the potential to slow. This creates opportunities for threat actors to infiltrate undetected. Without real-time autonomous systems in place, such attacks can have a far more severe impact on an organization’s ability to respond and recover effectively.

Ransomware is among the most common threats targeting organizations after hours. In 76% of cases, the encryption process begins during off-hours or on weekends6. For instance, Darktrace identified a ransomware attack launched in the early hours of Christmas Day on a client’s network, taking advantage of the period when most employees were offline.

Festive cheer: giving your SOC team the break they deserve

Staff burnout is increasingly top of mind, with 74% of cybersecurity leaders reporting that they’ve had employees resign due to stress7. And the numbers stack up – almost 60% of security analysts report feeling burnt out, and many are choosing to leave their jobs and even security altogether.8

At a human level, the holiday season should be a time of relaxation and merriment rather than anxiety. For SOC leaders, giving teams time to prioritize recharging during the holidays is crucial for sustaining long-term resilience and productivity, balanced with the importance of maintaining rigorous defenses with a reduced workforce.  

So… how can cybersecurity leaders ensure peace of mind during the holidays?

Step 1: Cover yourself from every angle. It’s no longer enough for your email solution to only catch known threats. Security leaders need to invest in multi-layered email defenses that can combat novel and advanced attacks – such as the multi-stage brand personation attacks that lead shoppers to domain-spoofed websites.  

Darktrace / EMAIL – the fastest growing email security solution – has been proven to detect up to 56% more threats than other email solutions.9  It is uniquely capable of catching novel attacks on the first encounter, rather than waiting the 13 days it takes for other solutions to take action10 – by which time your decorations might be coming down, along with your business.

Step 2: Avoid an overwhelming deluge of alerts raining (or snowing) down on your L1 SOC analysts. Lining up people to manage the grunt work over the holidays is an easy pattern to fall into, but consider technology that can automate that initial triage. For example, Darktrace’s Cyber AI Analyst automatically investigates every alert detected by Darktrace’s core real-time detection engine. It does an additional layer of AI analysis – establishing whether an alert is unusual but benign, or part of a more serious security incident. Rather than looking at hundreds of alerts, your team is presented with just a handful of overall incidents. They can use that new free time to do more strategic work, or take some much-needed time off.

Step 3: Make sure someone – or something – is keeping guard in those super off-peak hours. Enter Autonomous Response. Because it knows what normal looks like for your business it can take action to stop and contain only the unusual and threatening activity. Even if it doesn’t eliminate the threat entirely, it can buy your security team time and space, allowing them to enjoy their holiday in peace.

With Black Friday over and the festive shopping period looming, businesses should act now to protect their brand and ensure they have the cybersecurity measures are in place to enjoy the gift of a stress-free holiday season.  

Interested in how AI-driven email security can protect your organization? Check out the product hub to learn more. Or watch the demo video to see Darktrace / EMAIL in action.

References

[1] Based on analysis of 626 customer deployments and attempted phishing emails mentioning Christmas that were detected by Darktrace / EMAIL.

[2] Emails in the analysis mentioning ‘Black Friday’ or ‘Cyber Monday’.

[3] Walmart, Target, Best Buy, Macy's, Old Navy, 1800-Flowers

[4] Amazon, eBay, Netflix, Alibaba, Paypal, Apple

[5] In 2021, Darktrace observed a 70% average increase in attempted ransomware attacks in November and December compared to January and February. (Darktrace Press Release, 2021)

[6] https://www.zdnet.com/article/most-ransomware-attacks-take-place-during-the-night-or-the-weekend

[7] https://www.scworld.com/perspective/ciso-stress-levels-are-out-of-control

[8] https://www.informationweek.com/cyber-resilience/the-psychology-of-cybersecurity-burnout

[9] 56% of malicious phishing emails detected and analyzed across Darktrace / EMAIL customer deployments from December 2023 – July 2024 passed through all existing security layers. (Darktrace Half Year Report 2024)

[10] 13 days 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. (Darktrace Press Release, 2023)

No items found.
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.
No items found.

More in this series

No items found.

Blog

/

/

July 17, 2025

Introducing the AI Maturity Model for Cybersecurity

AI maturity model for cybersecurityDefault blog imageDefault blog image

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

[related-resource]

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.

[related-resource]

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.

Continue reading
About the author

Blog

/

/

July 17, 2025

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

people working and walking in officeDefault blog imageDefault blog image

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.

[related-resource]

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.

[related-resource]

Continue reading
About the author
Your data. Our AI.
Elevate your network security with Darktrace AI