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June 9, 2021

Multi-Account Hijack Detection with AI

Discover the analysis of a sophisticated SaaS-based attack using Microsoft 365 accounts. Learn how attackers launch & maintain their offensive 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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09
Jun 2021

The widespread and rapid adoption of Software-as-a-Service (SaaS) has opened up a breadth of security risks for IT teams. Unlike commercial off-the-shelf (COTS) software, SaaS security tends to be managed by third-party vendors rather than the end customer. Security teams therefore struggle with reduced visibility and control over these environments, and cyber-criminals have been quick to take advantage, launching a wave of cloud-based attacks, from Vendor Email Compromise to internal account hijacks.

Attackers often gain access to multiple accounts on the same domain, enabling them to attack from multiple angles, for example sending of hundreds of emails from one account, while maintaining persistence with another. This gives the hacker an opportunity to try multiple attack vectors, using tools native to the SaaS environment as well as external payloads.

While preventative controls such as Multi-Factor Authentication (MFA) provide an extra layer of protection, there are many techniques available to circumvent zero-trust approaches. Remote and flexible working is set to continue to varying degrees across many different regions and industries, so companies must now commit to securing their cloud architecture and developing proactive cyber security measures.

In this blog, we will analyze a persistent cyber-attack which targeted a real estate company in Europe and leveraged several compromised Microsoft 365 accounts. These SaaS takeovers are quickly becoming the new norm, but they are still misunderstood and poorly documented in the wider industry. Cyber AI detected every stage of this intrusion in real time, without the use of signatures or static rules.

A and B: Hijacking Microsoft 365 accounts

The organization had around 5,000 devices in its environment, with 1,000 active SaaS accounts. The timeline below shows how the threat actor leveraged the SaaS accounts of five different users to carry out the operation, as well as exploiting several other accounts on the final day.

Figure 1: Diagram of the infection chain, which occurred over three days. On the fourth day, the attacker tried again but was unsuccessful.

The actor initially compromised at least two SaaS credentials – which we’ll refer to here simply as ‘account A’ and ‘account B’ – and logged in from several unusual geographical locations, presumably using a VPN. Darktrace detected this as unusual login events for the SaaS accounts.

In account A, the attacker was observed previewing files likely to contain customer information, but did not perform any other follow-up activity. In account B, they set a new inbox rule three hours after the initial compromise, resulting in a high-severity alert.

At around this time, the threat actor sent a number of phishing emails from account B: emails that appeared to be sharing a harmless and legitimate-looking folder on OneDrive. The link probably led to a fake Microsoft login page, similar to the below, which could have recorded the victims’ credentials and sent them directly back to the attacker.

Figure 2: A seemingly legitimate Microsoft login page.

The phishing attempt was detected by Antigena Email, Darktrace’s email security technology. Antigena was in passive mode at the time, and so was not configured to take action on these threatening emails. But taking into account the highly anomalous sender surge coupled with the unusual login locations, it would have autonomously intercepted all the emails, reducing the impact of the attack.

The attacker was subsequently locked out of account B. After this, they tried (and failed) to use a legacy user agent to bypass any MFA which may have been enforced on the account. Darktrace detected this as a suspicious login and blocked the attempt.

Accounts C, D and E: The threat develops

The next day, the actor logged into a new account (account C) from the same autonomous system number (ASN), indicating that the account had been infected by the OneDrive phishing emails. In other words, the attacker had leveraged account B to compromise new users in the organization and ensure multiple points of intrusion.

Darktrace detected each stage of this, piecing together the different events into one meaningful security narrative.

Figure 3: Anomalous activity from accounts C, D, and E.

Account C was then used to preview a file likely containing contact information.

After being locked out of account C when trying to log in the next day, the hacker worked their way through two more accounts (account D and account E), which they had hijacked in the previous phishing attempts. They were locked out each time after generating alerts due to the unusual logins and new inbox rules created around the same time.

A to Z: End of the line

Running out of options, the attacker decided to go back to account A and set a new inbox rule, using it to send new phishing emails with a link to a non-Microsoft cloud storage domain (Tresorit). Again, Darktrace recognized this as highly unusual behavior, and the hacker was promptly locked out of the account.

During this burst of activity, Darktrace also observed a Microsoft Teams session from one of the suspicious ASNs. This was likely a social engineering attempt and another possible attack vector. Microsoft Teams could have been leveraged to share a malicious link over instant message, extract sensitive information, or send spam internally and externally on the chat function.

The threat actor could have then used this to pivot across various applications and accounts, assuming that the company had a siloed security approach – with different tools for cloud, SaaS, email, and endpoint – and so could not pick up on the malicious cross-platform movement.

On the following day, the attacker attempted logins on multiple accounts again, but with no success. Cyber AI had pinpointed all the anomalous activity – no matter where it originated – and alerted the security team immediately.

SaaS attack under the microscope

Multi-account compromises can be incredibly persistent and are difficult for traditional security tools to identify. The hacker used several tactics to circumvent the customer’s existing email security products:

  1. The initial use of two compromised credentials – account A and account B – allowed the hacker to stay under the radar and not raise too much suspicion on a single account. Account A was kept quiet until other avenues had been exhausted.
  2. Activity was generated from multiple ASNs in at least three different geographical locations, probably utilizing a VPN: one in Africa where much of the activity originated, and two in North America, including some widely used ASNs which were highly unusual for the customer.
  3. The attacker entirely used Microsoft services until the final emails, choosing to ‘live off the land’ rather than sending links that may have been caught by gateways.
  4. The attacker logged into Microsoft Teams in their final movements – a fairly benign-looking event which could have been used to compromise more accounts and move laterally, and would have gone undetected.

Darktrace identified every stage of the attack – including spotting the anomalous ASNs – and launched an automatic, in-depth investigation with Cyber AI Analyst. The organization was thus able to take action before the damage was done.

Figure 4: Darktrace’s SaaS console gives a clear overview of activity across all different applications.

ABCs of SaaS security

The approach of using various accounts to mount the offensive, while keeping one to maintain persistence, prolonged this intrusion. Such tactics will likely be seen again in the near future.

Tracking the number of factors involved in an attack with multiple credentials, multiple attack vectors, and multiple attacker-IPs, is a serious challenge. In these situations, it is essential to have a security solution which can detect activity across different applications, forming a unified and holistic understanding over the entire digital enterprise.

While not active in this case, Antigena SaaS would have taken autonomous action and prevented the threat from escalating by enforcing normal behavior, stopping the hacker from logging in from malicious infrastructure or performing any out-of-character SaaS actions, such as creating new inbox rules.

Following the intrusion, the company decided to adopt Antigena SaaS, which now mitigates their cloud security risks and guards against sensitive data loss and reputational damage.

Thanks to Darktrace analyst Daniel Gentle for his insights on the above threat find.

Darktrace model detections:

  • SaaS / Compromise / Unusual Login and New Email Rule
  • SaaS / Compliance / New Email Rule
  • SaaS / Unusual Activity / Unusual External Source for SaaS Credential Use
  • SaaS / Access / Suspicious Login Attempt
  • Antigena Email: Unusual Login Location + Sender Surge
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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December 23, 2025

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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