Blog
/
AI
/
September 6, 2023

Preparing Security Defenses For the AI Cyber Attack Era

The threat of AI being used in cyberattacks is growing. Learn how Darktrace is harnessing the power of AI to protect security systems against these attacks.
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
Jack Stockdale OBE FREng
Chief Technology Officer
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
06
Sep 2023

The last 12 months have been a watershed moment in the public perception and adoption of AI. With the rise of generative AI systems like ChatGPT and Google Bard, AI is becoming more embedded in our everyday lives and there is a lot of hype around what these tools can – or will - do.  

In cyber security, AI is a double-edged sword. Its use by cyber-attackers is still in its infancy, but Darktrace expects that the mass availability of generative AI tools like ChatGPT will significantly enhance attackers’ capabilities by providing better tools to generate and automate human-like attacks. There are three areas where Darktrace sees potential for AI to significantly enhance the capabilities of attackers: increasing the sophistication of low-level threat actors, increasing the speed of attacks through automation and eroding trust among users.

We’ve already started to see some potential indicators of these shifts.

In April, Darktrace revealed a 135% increase in ‘novel social engineering attacks’ – email attacks that show a strong linguistic deviation from other phishing emails – from January to February 2023 [1]. The timing corresponds with the widespread adoption of ChatGPT and suggests the use of generative AI tools is providing an avenue for threat actors to craft more sophisticated and targeted attacks, at speed and scale.

Between May and July this year, our Cyber AI Research Centre observed that multistage payload attacks, in which a malicious email encourages the recipient to follow a series of steps before delivering a payload or attempting to harvest sensitive information, have increased by an average of 59% across Darktrace customers. Nearly 50,000 more of these attacks were detected by Darktrace in July than May, indicating potential use of automation, and the speed of these types of attacks will likely rise as greater automation and AI are adopted and applied by attackers.

In the same period, Darktrace has seen changes in attacks that abuse trust. While VIP impersonation – phishing emails that mimic senior executives – decreased 11%, email account takeover attempts increased by 52% and impersonation of the internal IT team increased by 19% [2]. The changes suggest that as employees have become better attuned to the impersonation of senior executives, attackers are pivoting to impersonating IT teams to launch their attacks. While it’s common for attackers to pivot and adjust their techniques as efficacy declines, generative AI –  particularly deepfakes - has the potential to disrupt this pattern in favor of attackers. Factors like increasing linguistic sophistication and highly realistic voice deep fakes could more easily be deployed to deceive employees.

These early indicators give us a glimpse of a new era of disruption and challenges for cyber security. An era where novel is the new normal.

Darktrace was built for this moment.

Darktrace began ten years ago as an AI Research Centre. We saw that AI could address an existential threat – defending people, businesses and nations from a world of constantly evolving threats. This threat is only poised to grow as AI is increasingly used by attackers. That’s why we became one of the first to apply AI to cyber security and built a completely AI native technology platform aimed at freeing the world of cyber disruption.

We built everything at Darktrace with the same philosophy of using the right AI and the right data for the job.

Most AI today is trained periodically in offline training environments on huge amounts of combined historic training data. You give all that data to the AI, and then after a few days or weeks, you get a static AI model which you push live to serve its role until the next version is ready. This is ideal for tasks like generating imagery or, in cyber security, checking against known attack patterns, but the AI is static – it doesn’t learn or adapt until the next version is pushed live.

Darktrace takes a different and unique approach to nearly everyone else in cyber security. Our distinction lies in the algorithms we use, the data we use AND, most importantly, in how the two interact.  

Instead of taking your data to the AI, we take our AI to your data. Inside every single customer lies a Darktrace AI that is completely unique to them – their OWN data AI pipeline – plugged into their enterprise and self-learning in real time from everything that happens in their digital world –including email, cloud environments, manufacturing and operational systems, and physical locations.

The pace of new threats and the sophistication of the technology, including the use of AI, now outpaces any notion that a week old view of historic cyber threats can fully protect a business – either from the new threats that we’re seeing today from the sudden availability of generative AI tools, or the threats of tomorrow. For example, automated deepfakes where you can’t trust what you’re hearing or seeing, your employees being tricked into being inadvertent insiders, or self-evolving code designed to evade the best of those legacy defenses.

And because the increased use of AI in attacks will mean novel attacks will become the new normal, only Darktrace stands between those attacks succeeding or failing. We’ve seen this before with our technology detecting, and protecting customers against, Log4J, supply chain attacks like SolarWinds, the novel phishing scams we saw during the Covid-19 lockdowns, zero days like the Citrix Netscaler attack, novel ransomware worms such as WannaCry, or sophisticated nation-state attacks like APT35. We didn’t protect businesses because we were looking specifically for these threats, but we found them because every threat, whether known or novel, accidental or malicious, human or AI driven, impacts the customer, its people and its data.

The right AI for the right job

Today we’re on our 6th generation of Darktrace AI and, as we’ve innovated and developed, we’ve built a platform of applied AI techniques and algorithms that utilise Darktrace’s live, tailored knowledge of a business, to defend it alongside human security teams. Our focus has always been on using the right AI and the right data for the job, which is why our software uses:

  • A wide range of our own self-learning methods to understand new information and decide if something never seen before looks suspicious.
  • Real time Bayesian Probabilistic Methods allow models to be efficiently updated and controlled in real time.
  • Generative and applied AI run simulated phishing campaigns, tabletop exercises and realistic drills.
  • Deep-neural networks replicate the thought process of humans.
  • Graph theory understands the incredibly complex relationships between people, systems, organizations and supply chains.
  • Offensive AI techniques such as Generative Adversarial Networks (GANs) help to test and improve our ability to counter AI driven attacks.  
  • Natural language processing and large language models interpret and produce human consumable output.

This complex platform of AI tools and techniques, all sat within a business, focused on the customers’ data, brings a range of advantages in data privacy, explainability and data transfer costs. But its main achievement is the one we set out for ten years ago. It can provide protection that is always on - always learning, able to detect and stop the unusual, the suspicious and the novel – and, ultimately, to protect our customers from it. That’s what we’ve always done and that’s what we will continue to do, regardless of how the landscape shifts.


[1] Based on the average change in email attacks between January and February 2023 detected across Darktrace/Email deployments with control of outliers.

[2] Based on the change in the average number of emails assigned this classification per 10,000 emails on each Darktrace/Email deployment in May versus July 2023 (significantly more than 1,000 deployments in total).

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
Jack Stockdale OBE FREng
Chief Technology Officer

More in this series

No items found.

Blog

/

/

May 8, 2025

Anomaly-based threat hunting: Darktrace's approach in action

person working on laptopDefault blog imageDefault blog image

What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

References

  1. https://spur.us/context/191.96.106.219

Continue reading
About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

Blog

/

/

May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

woman working on laptopDefault blog imageDefault blog image

With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

[related-resource]

Continue reading
About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
Your data. Our AI.
Elevate your network security with Darktrace AI