March 4, 2019

The VR Goldilocks Problem and Value of Continued Recognition

Security and Operations Teams face challenges when it comes to visibility and recognition. Learn more about how we find a solution to the problems!

Written by

Max Heinemeyer

Global Field CISO

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

Max Heinemeyer

Global Field CISO

Mar 2019

First, some context about VR

Security Operations teams face two fundamental challenges when it comes to 'finding bad'.

The first is gaining and maintaining appropriate visibility into what is happening in our environments. Visibility is provided through data (e.g. telemetry, logs). The trinity of data sources for visibility concern accounts/credentials, devices, and network traffic.

The second challenge is getting good recognition within the scope of what is visible. Recognition is fundamentally about what alerting and workflows you can implement and automate in response to activity that is suspicious or malicious.

Visibility and Recognition each have their own different associated issues.

Visibility is a problem about what is and can be generated and either read as telemetry, or logged and stored locally, or shipped to a central platform. The timelines and completeness of what visibility you have can depend on factors such as how much data you can or can't store locally on devices that generate data - and for how long; what your data pipeline and data platform look like (e.g. if you are trying to centralise data for analysis); or the capability of host software agents you have to process certain information locally.

The constraints on visibility sets the bar for factors like coverage, timelines and completeness of what recognition you can achieve. Without visibility, we cannot recognize at all. With limited visibility, what we can recognize may not have much value. With the right visibility, we can still fail to recognise the right things. And with too much recognition, we can quickly overload our senses.

A good example of a technology that offers the opportunity to solve these challenges at the network layer is Darktrace. Their technology provides visibility, from a network traffic perspective, into data that concerns devices and the accounts/credentials associated with them. They then provide recognition on top of this by using Machine Learning (ML) models for anomaly detection. Their models alert on a wide range of activities that may be indicative of threat activity, (e.g. malware execution and command and control, a technical exploit, data exfiltration and so on).

The major advantage they provide, compared to traditional Intrusion Detection Systems (IDS) and other vendors who also use ML for network anomaly detection, is that you can a) adjust the sensitivity of their algorithms and b) build your own recognition for particular patterns of interest. For example, if you want to monitor what connections are made to one or two servers, you can set up alerts for any change to expected patterns. This means you can create and adjust custom recognition based on your enterprise context and tune it easily in response to how context changes over time.

The Goldilocks VR Matrix

Below is what we call the VR Goldilocks Matrix at PBX Group Security. We use it to assess technology, measure our own capability and processes, and ask ourselves hard questions about where we need to focus to get the most value from our budget, (or make cuts / shift investment) if we need to.

In the squares are some examples of what you (maybe) should think about doing if you find yourself there.

Important questions to ask about VR

One of the things about Visibility and Recognition is that it’s not a given they are ‘always on’. Sometimes there are failure modes for visibility (causing a downstream issue with recognition). And sometimes there are failure modes or conditions under which you WANT to pause recognition.

The key questions you must have answers to about this include:

Under what conditions might I lose visibility?
How would I know if I have?
Is that loss a blind spot (i.e. data is lost for a given time period)…
…or is it 'a temporal delay’ (e.g. a connection fails and data is batched for moving from A to B but that doesn’t happen for a few hours)?
What are the recognitions that might be impacted by either of the above?
What is my expectation for the SLA on those recognitions from ‘cause of alert’ to ‘response workflow’?
Under what conditions would I be willing to pause recognition, change the workflow for what happens upon recognition, or stop it all together?
What is the stacked ranked list of ‘must, should, could’ for all recognition and why?

Alerts. Alerts everywhere.

More often than not, Security Operations teams suffer the costs of wasted time due to noisy alerts from certain data sources. As a consequence, it's more difficult for them to single out malicious behavior as suspicious or benign. The number of alerts that are generated due to out of the box SIEM platform configurations for sources like Web Proxies and Domain Controllers are often excessive, and the cost to tune those rules can also be unpalatable. Therefore, rather than trying to tune alerts, teams might make a call to switch them off until someone can get around to figuring out a better way. There’s no use having hypothetical recognition, but no workflow to act on what is generate (other than compliance).

This is where technologies that use ML can help. There are two basic approaches...

One is to avoid alerting until multiple conditions are met that indicate a high probability of threat activity. In this scenario, rather than alerting on the 1st, 2nd, 3rd and 4th ‘suspicious activities’, you wait until you have a critical mass of indicators, and then you generate one high fidelity alert that has a much greater weighting to be malicious. This requires both a high level of precision and accuracy in alerting, and naturally some trade off in the time that can pass before an alert for malicious activity is generated.

The other is to alert on ‘suspicious actives 1-4' and let an analyst or automated process decide if this merits further investigation. This approach sacrifices accuracy for precision, but provides rapid context on whether one, or multiple, conditions are met that push the machine(s) up the priority list in the triage queue. To solve for the lower level of accuracy, this approach can make decisions about how long to sustain alerting. For example, if a host triggers multiple anomaly detection models, rather than continue to send alerts (and risk the SOC deciding to turn them off), the technology can pause alerts after a certain threshold. If a machine has not been quarantined or taken off the network after 10 highly suspicious behaviors are flagged, there is a reasonable assumption that the analyst will have dug into these and found the activity is legitimate.

Punchline 1: the value of Continued Recognition even when 'not malicious'

The topic of paused detections was raised after a recent joint exercise between PBX Group Security and Darktrace in testing Darktrace’s recognition. After a machine being used by the PBX Red Team breached multiple high priority models on Darktrace, the technology stopped alerting on further activity. This was because the initial alerts would have been severe enough to trigger a SOC workflow. This approach is designed to solve the problem of alert overload on a machine that is behaving anomalously but is not in fact malicious. Rather than having the SOC turn off alerts for that machine (which could later be used maliciously), the alerts are paused.

One of the outcomes of the test was that the PBX Detect team advised they would still want those alerts to exist for context to see what else the machine does (i.e. to understand its pattern of life). Now, rather than pausing alerts, Darktrace is surfacing this to customers to show where a rule is being paused and create an option to continue seeing alerts for a machine that has breached multiple models.

Which leads us on to our next point…

Punchline 2: the need for Atomic Tests for detection

Both Darktrace and Photobox Security are big believers in Atomic Red Team testing, which involves ‘unit tests’ that repeatedly (or at a certain frequency) test a detection using code. Unit tests automate the work of Red Teams when they discovery control strengths (which you want to monitor continuously for uptime) or control gaps (which you want to monitor for when they are closed). You could design atomic tests to launch a series of particular attacks / threat actor actions from one machine in a chained event. Or you could launch different discreet actions from different machines, each of which has no prior context for doing bad stuff. This allows you to scale the sample size for testing what recognition you have (either through ML or more traditional SIEM alerting). Doing this also means you don't have to ask Red Teams to repeat the same tests again, allowing them to focus on different threat paths to achieve objectives.

Mitre Att&ck is an invaluable framework for this. Many vendors are now aligning to Att&ck to show what they can recognize relating to attack TTPs (Tools, Tactics and Procedures). This enables security teams to map what TTPs are relevant to them (e.g. by using threat intel about the campaigns of threat actor groups that are targeting them). Atomic Red Team tests can then be used to assure that expected detections are operational or find gaps that need closing.

If you miss detections, then you know you need to optimise the recognition you have. If you get too many recognitions outside of the atomic test conditions, you either have to accept a high false positive rate because of the nature of the network, or you can tune your detection sensitivity. The opportunities to do this with technology based on ML and anomaly detection are significant, because you can quickly see for new attack types what a unit test tells you about your current detections and that coverage you think you have is 'as expected'.

Punchline 3: collaboration for the win

Using well-structured Red Team exercises can help your organisation and your technology partners learn new things about how we can collectively find and halt evil. They can also help defenders learn more about good assumptions to build into ML models, as well as covering edge cases where alerts have 'business intelligence' value vs ‘finding bad’.

If you want to understand the categorisations of ways that your populations of machines act over time, there is no better way to do it than through anomaly detection and feeding alerts into a system that supports SOC operations as well as knowledge management (e.g. a graph database).

Working like this means that we also help get the most out of the visibility and recognition we have. Security solutions can be of huge help to Network and Operations teams for troubleshooting or answering questions about network architecture. Often, it’s just a shift in perspective that unlocks cross-functional value from investments in security tech and process. Understanding that recognition doesn’t stop with security is another great example of where technologies that let you build your own logic into recognition can make a huge difference above protecting the bottom line, to adding top line value.

Written by

Max Heinemeyer

Global Field CISO

Inside the SOC

Written by

Max Heinemeyer

Global Field CISO

•

September 8, 2025

Emma Foulger

Global Threat Research Operations Lead

Network

•

September 9, 2025

Watch the NIS2 Webinar

Blog

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.

[related-resource]

About the author

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.

‍

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.