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.

Written by

Jack Stockdale OBE FREng

Chief Technology Officer

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

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 6^th 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.

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[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).

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Written by

Jack Stockdale OBE FREng

Chief Technology Officer

Inside the SOC

Written by

Jack Stockdale OBE FREng

Chief Technology Officer

VP, Security & AI Strategy, Field CISO

Senior Product Marketing Manager, Email

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Blog

Identity

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1. Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

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About the author

Carlos Gray

Senior Product Marketing Manager, Email

Blog

Proactive Security

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

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Vulnerabilities are weaknesses in a system that can be exploited by malicious actors to gain unauthorized access or to disrupt normal operations. Common Vulnerabilities and Exposures (or CVEs) are a list of publicly disclosed cybersecurity vulnerabilities that can be tracked and mitigated by the security community.

When a vulnerability is discovered, the standard practice is to report it to the vendor or the responsible organization, allowing them to develop and distribute a patch or fix before the details are made public. This is known as responsible disclosure.

With a record-breaking 40,000 CVEs reported for 2024 and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST) [1], anomaly-detection is essential for identifying these potential risks. The gap between exploitation of a zero-day and disclosure of the vulnerability can sometimes be considerable, and retroactively attempting to identify successful exploitation on your network can be challenging, particularly if taking a signature-based approach.

Detecting threats without relying on CVE disclosure

Abnormal behaviors in networks or systems, such as unusual login patterns or data transfers, can indicate attempted cyber-attacks, insider threats, or compromised systems. Since Darktrace does not rely on rules or signatures, it can detect malicious activity that is anomalous even without full context of the specific device or asset in question.

‍For example, during the Fortinet exploitation late last year, the Darktrace Threat Research team were investigating a different Fortinet vulnerability, namely CVE 2024-23113, for exploitation when Mandiant released a security advisory around CVE 2024-47575, which aligned closely with Darktrace’s findings.

Retrospective analysis like this is used by Darktrace’s threat researchers to better understand detections across the threat landscape and to add additional context.

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Often, the disclosure of an exploited vulnerability can be off the back of an incident response investigation related to a compromise by an advanced threat actor using a zero-day. Once the vulnerability is registered and publicly disclosed as having been exploited, it can kick off a race between the attacker and defender: attack vs patch.

Nation-state actors, highly skilled with significant resources, are known to use a range of capabilities to achieve their target, including zero-day use. Often, pre-CVE activity is “low and slow”, last for months with high operational security. After CVE disclosure, the barriers to entry lower, allowing less skilled and less resourced attackers, like some ransomware gangs, to exploit the vulnerability and cause harm. This is why two distinct types of activity are often seen: pre and post disclosure of an exploited vulnerability.

Darktrace saw this consistent story line play out during several of the Fortinet and PAN OS threat actor campaigns highlighted above last year, where nation-state actors were seen exploiting vulnerabilities first, followed by ransomware gangs impacting organizations [2].

The same applies with the recent SAP Netweaver exploitations being tied to a China based threat actor earlier this spring with subsequent ransomware incidents being observed [3].

Autonomous Response

Anomaly-based detection offers the benefit of identifying malicious activity even before a CVE is disclosed; however, security teams still need to quickly contain and isolate the activity.

For example, during the Ivanti chaining exploitation in the early part of 2025, a customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device.

This pre-CVE detection and response by Darktrace occurred 11 days before any public disclosure, demonstrating the value of an anomaly-based approach.

In some cases, customers have even reported that Darktrace stopped malicious exploitation of devices several days before a public disclosure of a vulnerability.

For example, During the ConnectWise exploitation, a customer informed the team that Darktrace had detected malicious software being installed via remote access. Upon further investigation, four servers were found to be impacted, while Autonomous Response had blocked outbound connections and enforced patterns of life on impacted devices.

Conclusion

By continuously analyzing behavioral patterns, systems can spot unusual activities and patterns from users, systems, and networks to detect anomalies that could signify a security breach.

Through ongoing monitoring and learning from these behaviors, anomaly-based security systems can detect threats that traditional signature-based solutions might miss, while also providing detailed insights into threat tactics, techniques, and procedures (TTPs). This type of behavioral intelligence supports pre-CVE detection, allows for a more adaptive security posture, and enables systems to evolve with the ever-changing threat landscape.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO), Emma Fougler (Global Threat Research Operations Lead), Ryan Traill (Analyst Content Lead)

References and further reading:

Related Darktrace blogs:

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*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

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Check out this article by Darktrace: Preparing Security Defenses For the AI Cyber Attack Era

Preparing Security Defenses For the AI Cyber Attack Era

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Top Eight Threats to SaaS Security and How to Combat Them

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Blog

Identity

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

The latest on the identity security landscape

What is SaaS security?

1. Account Takeover (ATO)

2. Privilege escalation

3. Lateral movement

4. Multi-Factor Authentication (MFA) bypass and session hijacking

5. OAuth token abuse

6. Insider threats

7. Application Programming Interface (API)-based attacks

8. Business Email Compromise (BEC) via SaaS

Protecting against these SaaS threats

AI-powered SaaS security stays ahead of the threat landscape

Blog

Proactive Security

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Detecting threats without relying on CVE disclosure

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Autonomous Response

Conclusion

References and further reading:

Related Darktrace blogs: