February 10, 2025

From Hype to Reality: How AI is Transforming Cybersecurity Practices

AI hype is everywhere, but not many vendors are getting specific. Darktrace’s multi-layered AI combines various machine learning techniques for behavioral analytics, real-time threat detection, investigation, and autonomous response.

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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.

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Feb 2025

AI is everywhere, predominantly because it has changed the way humans interact with data. AI is a powerful tool for data analytics, predictions, and recommendations, but accuracy, safety, and security are paramount for operationalization.

In cybersecurity, AI-powered solutions are becoming increasingly necessary to keep up with modern business complexity and this new age of cyber-threat, marked by attacker innovation, use of AI, speed, and scale. The emergence of these new threats calls for a varied and layered approach in AI security technology to anticipate asymmetric threats.

While many cybersecurity vendors are adding AI to their products, they are not always communicating the capabilities or data used clearly. This is especially the case with Large Language Models (LLMs). Many products are adding interactive and generative capabilities which do not necessarily increase the efficacy of detection and response but rather are aligned with enhancing the analyst and security team experience and data retrieval.

Consequently, many people erroneously conflate generative AI with other types of AI. Similarly, only 31% of security professionals report that they are “very familiar” with supervised machine learning, the type of AI most often applied in today’s cybersecurity solutions to identify threats using attack artifacts and facilitate automated responses. This confusion around AI and its capabilities can result in suboptimal cybersecurity measures, overfitting, inaccuracies due to ineffective methods/data, inefficient use of resources, and heightened exposure to advanced cyber threats.

Vendors must cut through the AI market and demystify the technology in their products for safe, secure, and accurate adoption. To that end, let’s discuss common AI techniques in cybersecurity as well as how Darktrace applies them.

Modernizing cybersecurity with AI

Machine learning has presented a significant opportunity to the cybersecurity industry, and many vendors have been using it for years. Despite the high potential benefit of applying machine learning to cybersecurity, not every AI tool or machine learning model is equally effective due to its technique, application, and data it was trained on.

Supervised machine learning and cybersecurity

Supervised machine models are trained on labeled, structured data to facilitate automation of a human-led trained tasks. Some cybersecurity vendors have been experimenting with supervised machine learning for years, with most automating threat detection based on reported attack data using big data science, shared cyber-threat intelligence, known or reported attack behavior, and classifiers.

In the last several years, however, more vendors have expanded into the behavior analytics and anomaly detection side. In many applications, this method separates the learning, when the behavioral profile is created (baselining), from the subsequent anomaly detection. As such, it does not learn continuously and requires periodic updating and re-training to try to stay up to date with dynamic business operations and new attack techniques. Unfortunately, this opens the door for a high rate of daily false positives and false negatives.

Unsupervised machine learning and cybersecurity

Unlike supervised approaches, unsupervised machine learning does not require labeled training data or human-led training. Instead, it independently analyzes data to detect compelling patterns without relying on knowledge of past threats. This removes the dependency of human input or involvement to guide learning.

However, it is constrained by input parameters, requiring a thoughtful consideration of technique and feature selection to ensure the accuracy of the outputs. Additionally, while it can discover patterns in data as they are anomaly-focused, some of those patterns may be irrelevant and distracting.

When using models for behavior analytics and anomaly detection, the outputs come in the form of anomalies rather than classified threats, requiring additional modeling for threat behavior context and prioritization. Anomaly detection performed in isolation can render resource-wasting false positives.

LLMs and cybersecurity

LLMs are a major aspect of mainstream generative AI, and they can be used in both supervised and unsupervised ways. They are pre-trained on massive volumes of data and can be applied to human language, machine language, and more.

With the recent explosion of LLMs in the market, many vendors are rushing to add generative AI to their products, using it for chatbots, Retrieval-Augmented Generation (RAG) systems, agents, and embeddings. Generative AI in cybersecurity can optimize data retrieval for defenders, summarize reporting, or emulate sophisticated phishing attacks for preventative security.

But, since this is semantic analysis, LLMs can struggle with the reasoning necessary for security analysis and detection consistently. If not applied responsibly, generative AI can cause confusion by “hallucinating,” meaning referencing invented data, without additional post-processing to decrease the impact or by providing conflicting responses due to confirmation bias in the prompts written by different security team members.‍

Combining techniques in a multi-layered AI approach

Each type of machine learning technique has its own set of strengths and weaknesses, so a multi-layered, multi-method approach is ideal to enhance functionality while overcoming the shortcomings of any one method.

Darktrace’s Self-Learning AI is a multi-layered engine is powered by multiple machine learning approaches, which operate in combination for cyber defense. This allows Darktrace to protect the entire digital estates of the organizations it secures, including corporate networks, cloud computing services, SaaS applications, IoT, Industrial Control Systems (ICS), and email systems.

Plugged into the organization’s infrastructure and services, our AI engine ingests and analyzes the raw data and its interactions within the environment and forms an understanding of the normal behavior, right down to the granular details of specific users and devices. The system continually revises its understanding about what is normal based on evolving evidence, continuously learning as opposed to baselining techniques.

This dynamic understanding of normal partnered with dozens of anomaly detection models means that the AI engine can identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign. Understanding anomalies through the lens of many models as well as autonomously fine-tuning the models’ performances gives us a higher understanding and confidence in anomaly detection.

The next layer provides event correlation and threat behavior context to understand the risk level of an anomalous event(s). Every anomalous event is investigated by Cyber AI Analyst that uses a combination of unsupervised machine learning models to analyze logs with supervised machine learning trained on how to investigate. This provides anomaly and risk context along with investigation outcomes with explainability.

The ability to identify activity that represents the first footprints of an attacker, without any prior knowledge or intelligence, lies at the heart of the AI system’s efficacy in keeping pace with threat actor innovations and changes in tactics and techniques. It helps the human team detect subtle indicators that can be hard to spot amid the immense noise of legitimate, day-to-day digital interactions. This enables advanced threat detection with full domain visibility.

Digging deeper into AI: Mapping specific machine learning techniques to cybersecurity functions

Visibility and control are vital for the practical adoption of AI solutions, as it builds trust between human security teams and their AI tools. That is why we want to share some specific applications of AI across our solutions, moving beyond hype and buzzwords to provide grounded, technical explanations.

Darktrace’s technology helps security teams cover every stage of the incident lifecycle with a range of comprehensive analysis and autonomous investigation and response capabilities.

Behavioral prediction: Our AI understands your unique organization by learning normal patterns of life. It accomplishes this with multiple clustering algorithms, anomaly detection models, Bayesian meta-classifier for autonomous fine-tuning, graph theory, and more.‍
Real-time threat detection: With a true understanding of normal, our AI engine connects anomalous events to risky behavior using probabilistic models.
‍Investigation: Darktrace performs in-depth analysis and investigation of anomalies, in particular automating Level 1 of a SOC team and augmenting the rest of the SOC team through prioritization for human-led investigations. Some of these methods include supervised and unsupervised machine learning models, semantic analysis models, and graph theory.‍
Response: Darktrace calculates the proportional action to take in order to neutralize in-progress attacks at machine speed. As a result, organizations are protected 24/7, even when the human team is out of the office. Through understanding the normal pattern of life of an asset or peer group, the autonomous response engine can isolate the anomalous/risky behavior and surgically block. The autonomous response engine also has the capability to enforce the peer group’s pattern of life when rare and risky behavior continues.‍
Customizable model editor: This layer of customizable logic models tailors our AI’s processing to give security teams more visibility as well as the opportunity to adapt outputs, therefore increasing explainability, interpretability, control, and the ability to modify the operationalization of the AI output with auditing.

See the complete AI architecture in the paper “The AI Arsenal: Understanding the Tools Shaping Cybersecurity.”

Figure 1. Alerts can be customized in the model editor in many ways like editing the thresholds for rarity and unusualness scores above.

Machine learning is the fundamental ally in cyber defense

Traditional security methods, even those that use a small subset of machine learning, are no longer sufficient, as these tools can neither keep up with all possible attack vectors nor respond fast enough to the variety of machine-speed attacks, given their complexity compared to known and expected patterns.

Security teams require advanced detection capabilities, using multiple machine learning techniques to understand the environment, filter the noise, and take action where threats are identified.

Darktrace’s Self-Learning AI comes together to achieve behavioral prediction, real-time threat detection and response, and incident investigation, all while empowering your security team with visibility and control.

Learn how AI is Applied in Cybersecurity

Discover specifically how Darktrace applies different types of AI to improve cybersecurity efficacy and operations in this technical paper.

Download the full report

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Inside the SOC

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September 8, 2025

Emma Foulger

Global Threat Research Operations Lead

Product Marketing Manager, Network Detection & Response

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Network

September 9, 2025

The benefits of bringing together network and email security

In many organizations, network and email security operate in isolation. Each solution is tasked with defending its respective environment, even though both are facing the same advanced, multi-domain threats.

This siloed approach overlooks a critical reality: email remains the most common vector for initiating cyber-attacks, while the network is the primary stage on which those attacks progress. Without direct integration between these two domains, organizations risk leaving blind spots that adversaries can exploit.

A modern security strategy needs to unify email and network defenses, not just in name, but in how they share intelligence, conduct investigations, and coordinate response actions. Let’s take a look at how this joined-up approach delivers measurable technical, operational, and commercial benefits.

Technical advantages

Pre-alert intelligence: Gathering data before the threat strikes

Most security tools start working when something goes wrong – an unusual login, a flagged attachment, a confirmed compromise. But by then, attackers may already be a step ahead.

By unifying network and email security under a single AI platform (like the Darktrace Active AI Security Platform), you can analyze patterns across both environments in real time, even when there are no alerts. This ongoing monitoring builds a behavioral understanding of every user, device, and domain in your ecosystem.

That means when an email arrives from a suspicious domain, the system already knows whether that domain has appeared on your network before – and whether its behavior has been unusual. Likewise, when new network activity involves a domain first spotted in an email, it’s instantly placed in the right context.

This intelligence isn’t built on signatures or after-the-fact compromise indicators – it’s built on live behavioral baselines, giving your defenses the ability to flag threats before damage is done.

Alert-related intelligence: Connecting the dots in real time

Once an alert does fire, speed and context matter. The Darktrace Cyber AI Analyst can automatically investigate across both environments, piecing together network and email evidence into a single, cohesive incident.

Instead of leaving analysts to sift through fragmented logs, the AI links events like a phishing email to suspicious lateral movement on the recipient’s device, keeping the full attack chain intact. Investigations that might take hours – or even days – can be completed in minutes, with far fewer false positives to wade through.

This is more than a time-saver. It ensures defenders maintain visibility after the first sign of compromise, following the attacker as they pivot into network infrastructure, cloud services, or other targets. That cross-environment continuity is impossible to achieve with disconnected point solutions or siloed workflows.

Operational advantages

Streamlining SecOps across teams

In many organizations, email security is managed by IT, while network defense belongs to the SOC. The result? Critical information is scattered between tools and teams, creating blind spots just when you need clarity.

When email and network data flow into a single platform, everyone is working from the same source of truth. SOC analysts gain immediate visibility into email threats without opening another console or sending a request to another department. The IT team benefits from the SOC’s deeper investigative context.

The outcome is more than convenience: it’s faster, more informed decision-making across the board.

Reducing time-to-meaning and enabling faster response

A unified platform removes the need to manually correlate alerts between tools, reducing time-to-meaning for every incident. Built-in AI correlation instantly ties together related events, guiding analysts toward coordinated responses with higher confidence.

Instead of relying on manual SIEM rules or pre-built SOAR playbooks, the platform connects the dots in real time, and can even trigger autonomous response actions across both environments simultaneously. This ensures attacks are stopped before they can escalate, regardless of where they begin.

Commercial advantages

While purchasing “best-of-breed" for all your different tools might sound appealing, it often leads to a patchwork of solutions with overlapping costs and gaps in coverage. However good a “best-in-breed" email security solution might be in the email realm, it won't be truly effective without visibility across domains and an AI analyst piecing intelligence together. That’s why we think “best-in-suite" is the only “best-in-breed" approach that works – choosing a high-quality platform ensures that every new capability strengthens the whole system.

On top of that, security budgets are under constant pressure. Managing separate vendors for email and network defense means juggling multiple contracts, negotiating different SLAs, and stitching together different support models.

With a single provider for both, procurement and vendor management become far simpler. You deal with one account team, one support channel, and one unified strategy for both environments. If you choose to layer on managed services, you get consistent expertise across your whole security footprint.

Even more importantly, an integrated AI platform sets the stage for growth. Once email and network are under the same roof, adding coverage for other attack surfaces – like cloud or identity – is straightforward. You’re building on the same architecture, not bolting on new point solutions that create more complexity.

Check out the white paper, The Modern Security Stack: Why Your NDR and Email Security Solutions Need to Work Together, to explore these benefits in more depth, with real-world examples and practical steps for unifying your defenses.

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About the author

Mikey Anderson

Product Marketing Manager, Network Detection & Response

Blog

September 9, 2025

Unpacking the Salesloft Incident: Insights from Darktrace Observations

Introduction

On August 26, 2025, Google Threat intelligence Group released a report detailing a widespread data theft campaign targeting the sales automation platform Salesloft, via compromised OAuth tokens used by the third-party Drift AI chat agent [1][2]. The attack has been attributed to the threat actor UNC6395 by Google Threat Intelligence and Mandiant [1].

The attack is believed to have begun in early August 2025 and continued through until mid-August 2025 [1], with the threat actor exporting significant volumes of data from multiple Salesforce instances [1]. Then sifting through this data for anything that could be used to compromise the victim’s environments such as access keys, tokens or passwords. This had led to Google Threat Intelligence Group assessing that the primary intent of the threat actor is credential harvesting, and later reporting that it was aware of in excess of 700 potentially impacted organizations [3].

Salesloft previously stated that, based on currently available data, customers that do not integrate with Salesforce are unaffected by this campaign [2]. However, on August 28, Google Threat Intelligence Group announced that “Based on new information identified by GTIG, the scope of this compromise is not exclusive to the Salesforce integration with Salesloft Drift and impacts other integrations” [2]. Google Threat Intelligence has since advised that any and all authentication tokens stored in or connected to the Drift platform be treated as potentially compromised [1].

This campaign demonstrates how attackers are increasingly exploiting trusted Software-as-a-Service (SaaS) integrations as a pathway into enterprise environment.

By abusing these integrations, threat actors were able to exfiltrate sensitive business data at scale, bypassing traditional security controls. Rather than relying on malware or obvious intrusion techniques, the adversaries leveraged legitimate credentials and API traffic that resembled legitimate Salesforce activity to achieve their goals. This type of activity is far harder to detect with conventional security tools, since it blends in with the daily noise of business operations.

The incident underscores the escalating significance of autonomous coverage within SaaS and third-party ecosystems. As businesses increasingly depend on interconnected platforms, visibility gaps become evident that cannot be managed by conventional perimeter and endpoint defenses.

By developing a behavioral comprehension of each organization's distinct use of cloud services, anomalies can be detected, such as logins from unexpected locations, unusually high volumes of API requests, or unusual document activity. These indications serve as an early alert system, even when intruders use legitimate tokens or accounts, enabling security teams to step in before extensive data exfiltration takes place

What happened?

The campaign is believed to have started on August 8, 2025, with malicious activity continuing until at least August 18. The threat actor, tracked as UNC6395, gained access via compromised OAuth tokens associated with Salesloft Drift integrations into Salesforce [1]. Once tokens were obtained, the attackers were able to issue large volumes of Salesforce API requests, exfiltrating sensitive customer and business data.

Initial Intrusion

The attackers first established access by abusing OAuth and refresh tokens from the Drift integration. These tokens gave them persistent access into Salesforce environments without requiring further authentication [1]. To expand their foothold, the threat actor also made use of TruffleHog [4], an open-source secrets scanner, to hunt for additional exposed credentials. Logs later revealed anomalous IAM updates, including unusual UpdateAccessKey activity, which suggested attempts to ensure long-term persistence and control within compromised accounts.

Internal Reconnaissance & Data Exfiltration

Once inside, the adversaries began exploring the Salesforce environments. They ran queries designed to pull sensitive data fields, focusing on objects such as Cases, Accounts, Users, and Opportunities [1]. At the same time, the attackers sifted through this information to identify secrets that could enable access to other systems, including AWS keys and Snowflake credentials [4]. This phase demonstrated the opportunistic nature of the campaign, with the actors looking for any data that could be repurposed for further compromise.

Lateral Movement

Salesloft and Mandiant investigations revealed that the threat actor also created at least one new user account in early September. Although follow-up activity linked to this account was limited, the creation itself suggested a persistence mechanism designed to survive remediation efforts. By maintaining a separate identity, the attackers ensured they could regain access even if their stolen OAuth tokens were revoked.

Accomplishing the mission

The data taken from Salesforce environments included valuable business records, which attackers used to harvest credentials and identify high-value targets. According to Mandiant, once the data was exfiltrated, the actors actively sifted through it to locate sensitive information that could be leveraged in future intrusions [1]. In response, Salesforce and Salesloft revoked OAuth tokens associated with Drift integrations on August 20 [1], a containment measure aimed at cutting off the attackers’ primary access channel and preventing further abuse.

How did the attack bypass the rest of the security stack?

The campaign effectively bypassed security measures by using legitimate credentials and OAuth tokens through the Salesloft Drift integration. This rendered traditional security defenses like endpoint protection and firewalls ineffective, as the activity appeared non-malicious [1]. The attackers blended into normal operations by using common user agents and making queries through the Salesforce API, which made their activity resemble legitimate integrations and scripts. This allowed them to operate undetected in the SaaS environment, exploiting the trust in third-party connections and highlighting the limitations of traditional detection controls.

Darktrace Coverage

Anomalous activities have been identified across multiple Darktrace deployments that appear associated with this campaign. This included two cases on customers based within the United States who had a Salesforce integration, where the pattern of activities was notably similar.

On August 17, Darktrace observed an account belonging to one of these customers logging in from the rare endpoint 208.68.36[.]90, while the user was seen active from another location. This IP is a known indicator of compromise (IoC) reported by open-source intelligence (OSINT) for the campaign [2].

Figure 1: Cyber AI Analyst Incident summarizing the suspicious login seen for the account.

The login event was associated with the application Drift, further connecting the events to this campaign.

Figure 2: Advanced Search logs showing the Application used to login.

Following the login, the actor initiated a high volume of Salesforce API requests using methods such as GET, POST, and DELETE. The GET requests targeted endpoints like /services/data/v57.0/query and /services/data/v57.0/sobjects/Case/describe, where the former is used to retrieve records based on a specific criterion, while the latter provides metadata for the Case object, including field names and data types [5,6].

Subsequently, a POST request to /services/data/v57.0/jobs/query was observed, likely to initiate a Bulk API query job for extracting large volumes of data from the Ingest Job endpoint [7,8].

Finally, a DELETE request to remove an ingestion job batch, possibly an attempt to obscure traces of prior data access or manipulation.

A case on another US-based customer took place a day later, on August 18. This again began with an account logging in from the rare IP 208.68.36[.]90 involving the application Drift. This was followed by Salesforce GET requests targeting the same endpoints as seen in the previous case, and then a POST to the Ingest Job endpoint and finally a DELETE request, all occurring within one minute of the initial suspicious login.

The chain of anomalous behaviors, including a suspicious login and delete request, resulted in Darktrace’s Autonomous Response capability suggesting a ‘Disable user’ action. However, the customer’s deployment configuration required manual confirmation for the action to take effect.

Figure 3: An example model alert for the user, triggered due to an anomalous API DELETE request.

Figure 4: Model Alert Event Log showing various model alerts for the account that ultimately led to an Autonomous Response model being triggered.

Conclusion

In conclusion, this incident underscores the escalating risks of SaaS supply chain attacks, where third-party integrations can become avenues for attacks. It demonstrates how adversaries can exploit legitimate OAuth tokens and API traffic to circumvent traditional defenses. This emphasizes the necessity for constant monitoring of SaaS and cloud activity, beyond just endpoints and networks, while also reinforcing the significance of applying least privilege access and routinely reviewing OAuth permissions in cloud environments. Furthermore, it provides a wider perspective into the evolution of the threat landscape, shifting towards credential and token abuse as opposed to malware-driven compromise.

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Credit to Emma Foulger (Global Threat Research Operations Lead), Calum Hall (Technical Content Researcher), Signe Zaharka (Principal Cyber Analyst), Min Kim (Senior Cyber Analyst), Nahisha Nobregas (Senior Cyber Analyst), Priya Thapa (Cyber Analyst)

Appendices

Darktrace Model Detections

· SaaS / Access / Unusual External Source for SaaS Credential Use

· SaaS / Compromise / Login From Rare Endpoint While User Is Active

· SaaS / Compliance / Anomalous Salesforce API Event

· SaaS / Unusual Activity / Multiple Unusual SaaS Activities

· Antigena / SaaS / Antigena Unusual Activity Block

· Antigena / SaaS / Antigena Suspicious Source Activity Block

Customers should consider integrating Salesforce with Darktrace where possible. These integrations allow better visibility and correlation to spot unusual behavior and possible threats.