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April 9, 2024

The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners

Part 1: This blog outlines Darktrace’s State of AI Cybersecurity research report, showing key findings from our global survey, covering the impacts AI has on the cyber threat landscape, cyber security solutions, and perceptions and priorities for security practitioners.
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
Mitchell Bezzina
VP, Product and Solutions Marketing
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09
Apr 2024

What is the State of AI Cybersecurity Report?

We surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

Here are some of the key findings from the report:

What is the impact of AI on the cyber threat landscape?

Today’s security stakeholders are already seeing AI’s impact on the threat landscape.

"74% of survey respondents agree that AI-powered cyber threats are having a significant impact on their organizations. However, 60% of respondents fear that their organizations are not adequately prepared to defend against AI-powered threats and attacks."

How is AI being applied in cyber-attacks?

Generative AI can be used to create large volumes of highly personalized phishing attacks and to change the signatures and hashes associated with malware files. Other AI tools can also scan environments for exploitable vulnerabilities.

However, operationalizing AI in a cyber-attack requires sophistication. In most cases, attackers tend to begin using AI by addressing the simplest use cases or “lowest-hanging fruit.”

Identifying exactly when and where AI is being applied is not always possible since there are few methods for doing so. Thus, defenders will need to focus their effort on preparing for threats that are coming at them faster than ever before.

How does AI affect cyber risk?

"71% of organizations have already taken strides to reduce the risks that come with AI’s adoption."

In terms of cyber risk, adopting AI technologies into the business also generates concern for industry professionals given the increased risk of exposing sensitive or proprietary information through employee use of third-party generative AI tools. The access to publicly-available, text-based generative AI systems to increase productivity opens the door to “shadow AI” in which individuals use these popular AI tools without organizational approval or oversight.

What is the impact of AI on cybersecurity solutions?

AI is poised to transform not just the threat landscape but the solution landscape as well, a fact defenders understand.

"95% of cybersecurity professionals agree that AI-powered solutions will level up their organizations’ defenses."

Survey participants believe that AI-powered security solutions are a must-have for countering the risks posed by AI-powered threats. However, cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the increasing demands. But not all AI is created equal, and not all these solutions live up to the widespread hype.

"Improving threat detection (57%) and identifying exploitable vulnerabilities (50%) are the top ranked areas where respondents believe AI will make an impact."

However, survey participants may not fully understand how AI is applied to these aspects of cybersecurity. For example, generativeAI actually has little to no role to play in threat detection and proactive attack surface management. Generative AI does accelerate the data retrieval process within threat detection, can create quick incident summaries, automate low level tasks, and simulate phishing emails, but it does not improve the ability to detect novel attacks.

Understanding AI technologies in cybersecurity

A worldwide preoccupation with generativeAI may have colored perceptions of what AI is and where it’s most effectively applied.

"Only 26% of security professionals report a full understanding of the different types of AI in use within security products."

As the AI revolution unfolds, the speed at which vendors are introducing new AI-powered solutions far outpaces the rate at which practitioners are being trained how to use them.

There’s a strong need for greater vendor transparency, as well as efforts to educate end users so that they can better understand the technologies they are deploying.

Types of AI in cybersecurity

Supervised machine learning: Applied more often than any other type of AI in cybersecurity.Trained on human attack patterns and historical threat intelligence.

Natural language processing (NLP): Applies computational techniques to process and understand human language.

Large language models (LLMs): Applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. Used in generative AI tools. The integrity of their output depends upon the quality of the data on which they were trained.

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.

The more attention AI technology gets in cybersecurity, the higher expectations tend to be. As leaders and practitioners discover more about AI, they will need to learn when and where to use it – and how to offset the potential risks that various models and approaches can bring.

Cybersecurity practitioners’ priorities and objectives

Although security stakeholders are aware that the rise of AI will require them to implement new tools and deploy more advanced capabilities in certain areas, they still entertain multiple different – and sometimes conflicting – opinions about planning for the future.

"88% of cybersecurity professionals prefer a platform approach over individual point products."

Respondents expressed a strong preference for a platform- centric approach in their cybersecurity solution stacks. This is undoubtedly due to a far-reaching desire to reduce cost and complexity.

Even more widespread was agreement that organizations prefer to purchase new security capabilities within a broader platform rather than as individual point products.

"Top priorities for improving their ability to defend against AI-driven threats include adding AI-powered tools to their solution stacks and improving toolset integration."

Many security teams are looking to their existing vendors first when thinking about adding AI-powered tools to their solution stack. This may be because:

  1. It takes more time and effort to replace existing tooling than it does to add onto the exiting stack.
  2. Trust has already been established within existing relationships. As long as this is valued, there will always be a need to integrate AI and non-AI solutions.

Download the report for more statistics and insight on the state of AI in cybersecurity.

Learn more about AI can help you secure your enterprise

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
Mitchell Bezzina
VP, Product and Solutions Marketing

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July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

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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

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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:

  1. https://www.first.org/blog/20250607-Vulnerability-Forecast-for-2025
  2. https://cloud.google.com/blog/topics/threat-intelligence/fortimanager-zero-day-exploitation-cve-2024-47575
  3. https://thehackernews.com/2025/05/china-linked-hackers-exploit-sap-and.html

Related Darktrace blogs:

*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

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