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July 19, 2021

Data Exfiltration Trends in Latin America

Darktrace reveals key findings on data exfiltration in Latin America. Discover the latest cyber threats and defense strategies.
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
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19
Jul 2021

Data exfiltration is a popular enterprise for cyber-criminals. All organizations – from government agencies to small businesses – have sensitive data which can be stolen for extortion, competitive advantage, or further inroads in a company’s system. It is now the preferred technique of ransomware actors. Some Ransomware-as-a-Service (RaaS) groups have even pioneered a new type of ‘extortionware’, which focuses solely on data theft without encryption.

Easy money

At a pharmaceutical manufacturing institute in Latin America, Darktrace recently detected the exfiltration of critical files from the company, as this blog will explore.

The organization was an enticing target for two reasons. Firstly, pharmaceutical companies hold a wealth of valuable IP and patient data, which have come under sustained fire this last year as threat actors and nation states infiltrate vaccine research and distribution.

#1 most affected industry for data breaches: pharmaceuticals.

Secondly, Latin America is a treasure trove for cyber-criminals, thanks to huge economic growth in recent years, the digitization of major industries, alongside sub-standard cyber insurance policies and virtually nonexistent regulations.

Even before the COVID-19 pandemic, Brazil and Mexico were in Europol’s top ten affected countries. Since then, cases have skyrocketed – and many companies remain underprepared, facing limited support and pressure from government bodies. Strikingly, even though it has suffered estimated losses of almost $8 billion, Brazil still has no data protection law in place.

On top of financial crimes, the LATAM region has been targeted by state-sponsored groups linked to Russia, China, and Iran. Cyber-espionage is used as a method to gain the upper hand in negotiations and advance foreign interests in investment and trade.

Furthermore, as supply chains in the criminal world are hit by the effects of the pandemic, organized crime may begin to leverage the digital world – particularly fraud and phishing – as a possible source of income. La Familia Michoacana, a notorious drug cartel in Mexico, has reportedly begun enlisting Dark Web hackers.

Despite the number of threats facing Latin America, organizations have been slow to adopt defensive technology. So when the attacker in the case below chose a small organization in the LATAM region, they probably expected to face only signature-based, legacy security tools. Sensing that this would be easy prey – with little resistance and large profit to be made – the actor launched their first steps.

How the intrusion played out

During a Proof of Value trial with the company, Darktrace detected unusual activity from a server, following external remote connectivity.

Figure 1: Timeline of the attack.

The attack began when an internal server received an unusual connection over RDP from an external IP. The connection lasted five hours. The external IP then established a new SMB session to the same server using administrative credentials. The external IP leveraged SMB to access a file, which appeared to contain unencrypted passwords.

65% of the Colombian population now use the Internet, compared to only 3% in 2000.

From there, the external IP downloaded over 18,000 files over SMB. Based on the file names, it appears that the data was highly sensitive. In total, the external IP downloaded around 150 MB of data from the internal server.

Unusual activity post remote connections

Self-Learning AI detected that the IP address was 100% rare for the organization and server. The data transfer was also detected as unusual for the device’s ‘pattern of life’. Unfortunately, as Antigena was being trialled in passive mode, Darktrace could not intervene and disrupt the attack.

Nevertheless, Darktrace fired a number of high-confidence alerts to warn the security team. The figure below shows five-day activity from an example device in the same situation, with a high volume of clustered alerts. These reflect the unusual increase in volume transferred externally from a breach device.

Figure 2: A similar device received an incoming remote desktop connection, highlighted by the first model breach (orange dot). Shortly after, the external device accessed an unencrypted password file. At the same time, the device transferred an unusual volume of data to a rare external source IP.

Data exfiltration methods: RDP and password file access

The threat actor managed to bypass all the other existing security products in the company. They did this with legitimate administrative credentials, which were used to establish the RDP and SMB connections. RDP credentials are easily bought off the Dark Web and have proved a popular form of initial access, especially this year as employees continue to work remotely.

In addition, improper password management can unlock an organization’s digital kingdom. One of the accessed files was a password file, enabling the actor to quickly escalate privileges. After this point, only an AI-powered defensive tool could keep up with the speed of the intrusion.

Leveraging common protocols such as SMB to exfiltrate data is a common tactic. Internet-exposed servers are still a major risk to organizations as attackers exploit open and unused ports.

Moreover, the files transferred during the activity were saved as receipts with the names of partners and customers. This is extremely dangerous and could have put the company’s reputation at serious risk. Luckily, Self-Learning AI detected the malicious actions and warned the security team immediately, allowing them to stop further exfiltration and any follow-up activity.

Protecting sensitive data

The example above demonstrates that even the smallest of companies can fall victim to an attack. Small and medium-sized enterprises are targeted because they own important data and IP, yet often lack robust security and resources. This makes them simple catches compared to large establishments or governments.

Darktrace’s AI has the ability to detect malicious data exfiltration from subtle changes in behavior. In this case, the targeted server regularly transfers data in and out of the organization, yet Darktrace scored the incoming external IP with the highest rarity. In other words, Darktrace considered the data transfer activity highly unusual and outside of the server’s normal ‘pattern of life’.

This enabled the security team to respond to the threat and take the server offline for further investigation. If Darktrace Antigena had been active in the environment, it would have responded seconds after the initial compromise, stopping the threat at machine speed.

Thanks to Darktrace analyst Kendra Gonzalez Duran for her insights on the above threat find.

Learn how to defend your company from data exfiltration and malicious insiders

Darktrace model detections:

  • Compliance / Incoming Remote Desktop
  • Compliance / Possible Unencrypted Password File On Server
  • Anomalous Connection/ Data Sent to Rare Domain

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

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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