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April 20, 2022

Email Compromise To Mass Phishing Campaign

Read Darktrace's in-depth analysis on the shift from business email compromise to mass phishing campaigns. Gain the knowledge to safeguard your business.
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
Shuh Chin Goh
Written by
Sam Lister
Specialist Security Researcher
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20
Apr 2022

It is common for attackers to send large volumes of malicious emails from the email accounts which they compromise. Before carrying out this mass-mailing activity, there are predictable, preparatory steps which attackers take, such as registering mass-mailing applications and creating new inbox rules. In this blog, we will provide details of an attack observed in February 2022 in which a threat actor conducted a successful mass-mailing attack at a financial company based in Africa.

Attack summary

In February 2022, an attacker attempted to infiltrate the email environment of a financial services company based in Africa. At the beginning of February, the attacker likely gained a foothold in the company’s email environment by tricking an internal user into entering the credentials of their corporate email account into a phishing page. Over the following week, the attacker used the compromised account credentials to conduct a variety of activities, such as registering a mass-mailing application and creating a new inbox rule.

After taking these preparatory steps, the attacker went on to send out large volumes of phishing emails from the internal user’s email account. The attacker consequently obtained the credentials of several further internal corporate accounts. They used the credentials of one of these accounts to carry out similar preparatory steps (registering a mass-mailing application and creating a new inbox rule). After taking these steps, the attacker again sent large volumes of phishing emails from the account. At this point, the customer requested assistance from Darktrace’s SOC to aid investigation, and the intrusion was consequently contained by the company.

Since the attacker carried out their activities using a VPN and an Amazon cloud service, the endpoints from which the activities took place did not serve as particularly helpful indicators of an attack. However, prior to sending out phishing emails from internal users’ accounts, the attacker did carry out other predictable, preparatory activities. One of the main goals of this blog is to highlight that these behaviors serve as valuable signs of preparation for mass-mailing activity.

Attack timeline

Figure 1: Timeline of the intrusion

On February 3, the attacker sent a phishing email to the corporate account of an employee. The email was sent from the corporate account of an employee at a company with business ties to the victim enterprise. It is likely that the attacker had compromised this account prior to sending the phishing email from it. The phishing email in question claimed to be an overdue payment reminder. Within the email, there was a link hidden behind the display text “view invoice”. The hostname of the phishing link’s URL was a subdomain of questionpro[.]eu — an online survey platform. The page referred to by the URL was a fake Microsoft Outlook login page.

Figure 2: Destination of phishing link within the email sent by the attacker

Antigena Email, Darktrace’s email security solution, identified the highly unusual linguistic structure of the email, given its understanding of ‘normal’ for that sender. This was reflected in an inducement shift score of 100. However, in this case, the original URL of the phishing link was rewritten by Mimecast’s URL protection service in a way which made the full URL impossible to extract. Consequently, Antigena Email did not know what the original URL of the link was. Since the link was rewritten by Mimecast’s URL protection service, the email’s recipient will have received a warning notification in their browser upon clicking the link. It seems that the recipient ignored the warning, and consequently divulged their email account credentials to the attacker.

For Antigena Email to hold an email from a user’s mailbox, it must judge with high confidence that the email is malicious. In cases where the email contains no suspicious attachments or links, it is difficult for Antigena Email to obtain such high degrees of confidence, unless the email displays clear payload-independent malicious indicators, such as indicators of spoofing or indicators of extortion. In this case, the email, as seen by Antigena Email, didn’t contain any suspicious links or attachments (since Mimecast had rewritten the suspicious link) and the email didn’t contain any indicators of spoofing or extortion.

Figure 3: The email’s high inducement shift score highlights that the email’s linguistic content and structure were unusual for the email’s sender

Shortly after receiving the email, the internal user’s corporate device was observed making SSL connections to the questionpro[.]eu phishing endpoint. It is likely that the user divulged their email account credentials during these connections.

Figure 4: The above screenshot — obtained from Advanced Search — depicts the connections made by the account owner’s device on February 3

Between February 3 and February 7, the attacker logged into the user’s email account several times. Since these logins were carried out using a common VPN service, they were not identified as particularly unusual by Darktrace. However, during their login sessions, the attacker exhibited behavior which was highly unusual for the email account’s owner. The attacker was observed creating an inbox rule called “ _ ” on the user’s email account,[1] as well as registering and granting permissions to a mass-mailing application called Newsletter Software SuperMailer. These steps were taken by the attacker in preparation for their subsequent mass-mailing activity.

On February 7, the attacker sent out phishing emails from the user’s account. The emails were sent to hundreds of internal and external mailboxes. The email claimed to be an overdue payment reminder and it contained a questionpro[.]eu link hidden behind the display text “view invoice”. It is likely that the inbox rule created by the attacker caused all responses to this phishing email to be deleted. Attackers regularly create inbox rules on the email accounts which they compromise to ensure that responses to the malicious emails which they distribute are hidden from the accounts’ owners.[2]

Since Antigena Email does not have visibility of internal-to-internal emails, the phishing email was delivered fully weaponized to hundreds of internal mailboxes. On February 7, after the phishing email was sent from the compromised internal account, more than twenty internal devices were observed making SSL connections to the relevant questionpro[.]eu endpoint, indicating that many internal users had clicked the phishing link and possibly revealed their account credentials to the attacker.

Figure 5: The above screenshot — obtained from Advanced Search — depicts the large volume of connections made by internal devices to the phishing endpoint

Over the next five days, the attacker was observed logging into the corporate email accounts of at least six internal users. These logins were carried out from the same VPN endpoints as the attacker’s original logins. On February 11, the attacker was observed creating an inbox rule named “ , ” on one of these accounts. Shortly after, the attacker went on to register and grant permissions to the same mass-mailing application, Newsletter Software SuperMailer. As with the other account, these steps were taken by the attacker in preparation for subsequent mass-mailing activity.

Figure 6: The above screenshot — obtained from Advanced Search — outlines all of the actions involving the mass-mailing application that were taken by the attacker (accounts have been redacted)

On February 11, shortly after 08:30 (UTC), the attacker widely distributed a phishing email from this second user’s account. The phishing email was distributed to hundreds of internal and external mailboxes. Unlike the other phishing emails used by the attacker, this one claimed to be a purchase order notification, and it contained an HTML file named PurchaseOrder.html. Within this file, there was a link to a suspicious page on the public relations (PR) news site, everything-pr[.]com. After the phishing email was sent from the compromised internal account, more than twenty internal devices were observed making SSL connections to the relevant everything-pr[.]com endpoint, indicating that many internal users had opened the malicious attachment.

Figure 7: The above screenshot — obtained from Advanced Search — depicts the connections made by internal devices to the endpoint referenced in the malicious attachment

On February 11, the customer submitted an Ask the Expert (ATE) request to Darktrace’s SOC team. The guidance provided by the SOC helped the security team to contain the intrusion. The attacker managed to maintain a presence within the organization’s email environment for eight days. During these eight days, the attacker sent out large volumes of phishing emails from two corporate accounts. Before sending out these phishing emails, the attacker carried out predictable, preparatory actions. These actions included registering a mass-mailing application with Azure AD and creating an inbox rule.

Darktrace guidance

There are many learning points for this particular intrusion. First, it is important to be mindful of signs of preparation for malicious mass-mailing activity. After an attacker compromises an email account, there are several actions which they will likely perform before they send out large volumes of malicious emails. For example, they may create an inbox rule on the account, and they may register a mass-mailing application with Azure AD. The Darktrace models SaaS / Compliance / New Email Rule and SaaS / Admin / OAuth Permission Grant are designed to pick up on these behaviors.

Second, in cases where an attacker succeeds in sending out phishing emails from an internal, corporate account, it is advised that customers make use of Darktrace’s Advanced Search to identify users that may have divulged account credentials to the attacker. The phishing email sent from the compromised account will likely contain a suspicious link. Once the hostname of the link has been identified, it is possible to ask Advanced Search to display all HTTP or SSL connections to the host in question. If the hostname is www.example.com, you can get Advanced Search to display all SSL connections to the host by using the Advanced Search query, @fields.server_name:"www.example.com", and you can get Advanced Search to display all HTTP connections to the host by using the query, @fields.host:"www.example.com".

Third, it is advised that customers make use of Darktrace’s ‘watched domains’ feature[3] in cases where an attacker succeeds in sending out malicious emails from the accounts they compromise. If a hostname is added to the watched domains list, then a model named Compromise / Watched Domain will breach whenever an internal device is observed connecting to it. If Antigena Network is configured, then observed attempts to connect to the relevant host will be blocked if the hostname is added to the watched domains list with the ‘flag for Antigena’ toggle switched on. If an attacker succeeds in sending out a malicious email from an internal, corporate account, it is advised that customers add hostnames of phishing links within the email to the watched domains list and enable the Antigena flag. Doing so will cause Darktrace to identify and thwart any attempts to connect to the relevant phishing endpoints.

Figure 8: The above screenshot — obtained from the Model Editor — shows that Antigena Network prevented ten internal devices from connecting to phishing endpoints after the relevant phishing hostnames were added to the watched domains list on February 11

For Darktrace customers who want to find out more about phishing detection, refer here for an exclusive supplement to this blog.

MITRE ATT&CK techniques observed

Thanks to Paul Jennings for his contributions.

Footnotes

1. https://docs.microsoft.com/en-us/powershell/module/exchange/new-inboxrule?view=exchange-ps

2. https://www.fireeye.com/current-threats/threat-intelligence-reports/rpt-fin4.html

3. https://customerportal.darktrace.com/product-guides/main/watched-domains

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
Shuh Chin Goh
Written by
Sam Lister
Specialist Security Researcher

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Proactive Security

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January 7, 2026

How a leading bank is prioritizing risk management to power a resilient future

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As one of the region’s most established financial institutions, this bank sits at the heart of its community’s economic life – powering everything from daily transactions to business growth and long-term wealth planning. Its blend of physical branches and advanced digital services gives customers the convenience they expect and the personal trust they rely on. But as the financial world becomes more interconnected and adversaries more sophisticated, safeguarding that trust requires more than traditional cybersecurity. It demands a resilient, forward-leaning approach that keeps pace with rising threats and tightening regulatory standards.

A complex risk landscape demands a new approach

The bank faced a challenge familiar across the financial sector: too many tools, not enough clarity. Vulnerability scans, pen tests, and risk reports all produced data, yet none worked together to show how exposures connected across systems or what they meant for day-to-day operations. Without a central platform to link and contextualize this data, teams struggled to see how individual findings translated into real exposure across the business.

  • Fragmented risk assessments: Cyber and operational risks were evaluated in silos, often duplicated across teams, and lacked the context needed to prioritize what truly mattered.
  • Limited executive visibility: Leadership struggled to gain a complete, real-time view of trends or progress, making risk ownership difficult to enforce.
  • Emerging compliance pressure: This gap also posed compliance challenges under the EU’s Digital Operational Resilience Act (DORA), which requires financial institutions to demonstrate continuous oversight, effective reporting, and the ability to withstand and recover from cyber and IT disruptions.
“The issue wasn’t the lack of data,” recalls the bank’s Chief Technology Officer. “The challenge was transforming that data into a unified, contextualized picture we could act on quickly and decisively.”

As the bank advanced its digital capabilities and embraced cloud services, its risk environment became more intricate. New pathways for exploitation emerged, human factors grew harder to quantify, and manual processes hindered timely decision-making. To maintain resilience, the security team sought a proactive, AI-powered platform that could consolidate exposures, deliver continuous insight, and ensure high-value risks were addressed before they escalated.

Choosing Darktrace to unlock proactive cyber resilience

To reclaim control over its fragmented risk landscape, the bank selected Darktrace / Proactive Exposure Management™ for cyber risk insight. The solution’s ability to consolidate scanner outputs, pen test results, CVE data, and operational context into one AI-powered view made it the clear choice. Darktrace delivered comprehensive visibility the team had long been missing.

By shifting from a reactive model to proactive security, the bank aimed to:

  • Improve resilience and compliance with DORA
  • Prioritize remediation efforts with greater accuracy
  • Eliminate duplicated work across teams
  • Provide leadership with a complete view of risk, updated continuously
  • Reduce the overall likelihood of attack or disruption

The CTO explains: “We needed a solution that didn’t just list vulnerabilities but showed us what mattered most for our business – how risks connected, how they could be exploited, and what actions would create the biggest reduction in exposure. Darktrace gave us that clarity.”

Targeting the risks that matter most

Darktrace / Proactive Exposure Management offered the bank a new level of visibility and control by continuously analyzing misconfigurations, critical attack paths, human communication patterns, and high-value assets. Its AI-driven risk scoring allowed the team to understand which vulnerabilities had meaningful business impact, not just which were technically severe.

Unifying exposure across architectures

Darktrace aggregates and contextualizes data from across the bank’s security stack, eliminating the need to manually compile or correlate findings. What once required hours of cross-team coordination now appears in a single, continuously updated dashboard.

Revealing an adversarial view of risk

The solution maps multi-stage, complex attack paths across network, cloud, identity systems, email environments, and endpoints – highlighting risks that traditional CVE lists overlook.

Identifying misconfigurations and controlling gaps

Using Self-Learning AI, Darktrace / Proactive Exposure Management spots misconfigurations and prioritizes them based on MITRE adversary techniques, business context, and the bank’s unique digital environment.

Enhancing red-team and pen test effectiveness

By directing testers to the highest-value targets, Darktrace removes guesswork and validates whether defenses hold up against realistic adversarial behavior.

Supporting DORA compliance

From continuous monitoring to executive-ready reporting, the solution provides the transparency and accountability the bank needs to demonstrate operational resilience frameworks.

Proactive security delivers tangible outcomes

Since deploying Darktrace / Proactive Exposure Management, the bank has significantly strengthened its cybersecurity posture while improving operational efficiency.

Greater insight, smarter prioritization, stronger defensee

Security teams are now saving more than four hours per week previously spent aggregating and analyzing risk data. With a unified view of their exposure, they can focus directly on remediation instead of manually correlating multiple reports.

Because risks are now prioritized based on business impact and real-time operational context, they no longer waste time on low-value tasks. Instead, critical issues are identified and resolved sooner, reducing potential windows for exploitation and strengthening the bank’s ongoing resilience against both known and emerging threats.

“Our goal was to move from reactive to proactive security,” the CTO says. “Darktrace didn’t just help us achieve that, it accelerated our roadmap. We now understand our environment with a level of clarity we simply didn’t have before.”

Leadership clarity and stronger governance

Executives and board stakeholders now receive clear, organization-wide visibility into the bank’s risk posture, supported by consistent reporting that highlights trends, progress, and areas requiring attention. This transparency has strengthened confidence in the bank’s cyber resilience and enabled leadership to take true ownership of risk across the institution.

Beyond improved visibility, the bank has also deepened its overall governance maturity. Continuous monitoring and structured oversight allow leaders to make faster, more informed decisions that strategically align security efforts with business priorities. With a more predictable understanding of exposure and risk movement over time, the organization can maintain operational continuity, demonstrate accountability, and adapt more effectively as regulatory expectations evolve.

Trading stress for control

With Darktrace, leaders now have the clarity and confidence they need to report to executives and regulators with accuracy. The ability to see organization-wide risk in context provides assurance that the right issues are being addressed at the right time. That clarity is also empowering security analysts who no longer shoulder the anxiety of wondering which risks matter most or whether something critical has slipped through the cracks. Instead, they’re working with focus and intention, redirecting hours of manual effort into strategic initiatives that strengthen the bank’s overall resilience.

Prioritizing risk to power a resilient future

For this leading financial institution, Darktrace / Proactive Exposure Management has become the foundation for a more unified, data-driven, and resilient cybersecurity program. With clearer, business-relevant priorities, stronger oversight, and measurable efficiency gains, the bank has strengthened its resilience and met demanding regulatory expectations without adding operational strain.

Most importantly, it shifted the bank’s security posture from a reactive stance to a proactive, continuous program. Giving teams the confidence and intelligence to anticipate threats and safeguard the people and services that depend on them.

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About the author
Kelland Goodin
Product Marketing Specialist

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AI

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January 5, 2026

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
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