Over a dozen companies have suffered data theft attacks after a SaaS integration provider was breached and authentication tokens stolen.

While numerous cloud storage and SaaS vendors were targeted using the stolen tokens, BleepingComputer has learned that the majority of the data theft attacks targeted the cloud-based data warehouse platform Snowflake.

Snowflake confirmed “unusual activity” to BleepingComputer, stating that a small number of its customers were impacted.

“We recently detected unusual activity within a small number of Snowflake customer accounts linked to a specific third-party integration,” Snowflake told BleepingComputer.

“We immediately launched an investigation and, out of an abundance of caution, locked down potentially impacted customer accounts. We also notified potentially impacted customers and provided precautionary guidance to help them further protect their accounts.”

Snowflake stressed that the attacks did not involve any vulnerability or compromise of its systems.

As part of these attacks, the threat actor allegedly attempted to use the stolen authentication tokens to steal data from Salesforce, but was detected before they could succeed.

Data theft after alleged Anodot breach

While Snowflake would not confirm which third-party integration partner was linked to these attacks, BleepingComputer was told by numerous sources that the attacks stem from a security incident at data anomaly detection company Anodot.

Anodot is an AI-based analytics company that provides real-time anomaly detection for business and operational data, helping organizations automatically spot unusual changes in revenue, transactions, and system performance using machine learning. Data analytics company Glassbox acquired the company in November 2025.

BleepingComputer was told that numerous companies are now being extorted by the ShinyHunters extortion gang, which is demanding ransom payments to prevent the release of stolen data.

After learning of the attacks, the ShinyHunters group confirmed to BleepingComputer that they were behind them, claiming to have stolen data from dozens of companies this past Friday. They also confirmed their attempts to steal data from Salesforce, but said they were blocked by AI detection.

The blocked attempt comes amid a wave of data theft attacks over the past year targeting Salesforce customers.

The threat actors also claimed the attack stems from a security incident at Anodot, hinting that they allegedly had access to the company for some time.

The threat actor shared some of the companies allegedly affected by the incident, but BleepingComputer will not name them without confirmation.

Only one company, Payoneer, replied to our emails, stating that it aware of the integrator breach but was not impacted.

“We’re aware of a security incident involving a third-party service provider, Anodot. Based on our review, Payoneer has not been impacted,” Payoneer said in a statement to BleepingComputer.

Google’s Threat Intelligence Group, which has been tracking many of this year’s data theft campaigns, also confirmed to BleepingComputer that it is aware of the incident and is tracking it, but had nothing further to share at this time.

BleepingComputer has sent multiple emails to Anodot and its parent company, Glassbox, but has not yet received a reply.

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U.S. victims lost nearly $21 billion to cyber-enabled crimes last year, driven primarily by investment scams, business email compromise, tech support fraud, and data breaches, the Federal Bureau of Investigation says.

The figure continues the year-over-year record trend as it is up 26% compared to 2024, when Americans lost $16.6 billion to cybercrime.

A similar uptick was recorded in the number of complaints the Internet Crime Complaint Center (IC3) received, which surpassed 1 million last year, up from 859,000 the year before.

The most frequent complaints received last year referred to phishing attacks (191,000), extortion (89,000), and investment scams (72,000), which continued to drive massive losses.

Although smaller in absolute numbers, there were still a significant number of reports for serious attack types such as business email compromise (24,700 cases), data breaches (3,900), ransomware attacks (3,600), and SIM swapping (971).

Investment fraud accounted for 49% of all scam-related incidents recorded last year and resulted in losses of $8.6 billion. However, cybercrime targeting cryptocurrency caused the largest loss, exceeding $11 billion across 181,565 cases.

Cyber-enabled fraud was present in 453,000 complaints and accounted for $17.7 billion of the total losses submitted to the IC3 in 2025.

According to the IC3, Americans over the age of 60 were hit the hardest, with reported losses of $7.7 billion, a 37% increase compared to the previous year.

For the first time, the FBI’s report includes AI-related scams, which accounted for 22,300 complaints and $893 million in losses. These schemes involved voice cloning, fake profiles, forged documents, and deepfake videos.

In two cases, attacks targeting critical infrastructure (dams and nuclear facilities), the FBI labeled the incidents as data breaches.

The most targeted critical infrastructure sectors in 2025 were healthcare, manufacturing, financial services, information technology, and government facilities.

FBI fighting back

The FBI says that it has upgraded its efforts to block attacks, notify victims, and freeze stolen funds, in some cases even being able to retrieve them.

The agency initiated 3,900 Financial Fraud Kill Chain (FFKC) interventions in 2025, successfully blocking a portion of fraudulent transactions. Of the $1.16 billion targeted by attackers, the FBI froze $679 million.

Additional efforts from the agency to prevent cyber-enabled crimes included ‘Operation Level Up’ at the start of the year, a proactive approach to prevent financial losses by identifying and alerting victims of cryptocurrency investment fraud.

Of the 3,780 victims notified last year, 78% were unaware that they were being scammed.

The FBI recommends people not to rush when they receive urgent requests and face pressure tactics, and to use all available means to verify the authenticity of the communication before sending money or data.

Those who suspect compromise by hackers or scammers are urged to report the incidents with full details to ic3.gov.

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From being merely an auxiliary element, artificial intelligence has now become an integral part of the driving force behind businesses. Be it the analysis of large data sets or the execution of repetitive functions, the importance of artificial intelligence has already been demonstrated in multiple industries. However, the introduction of generative artificial intelligence has now added a new dimension to the capabilities that are being offered.

While traditional AI has been widely used for years, the rise of generative AI is making it important for businesses to understand how the two differ. Although these technologies are part of the same broad category, namely ‘artificial intelligence solutions,’ but they have very distinct functionalities and differences.

Understanding Traditional AI

Generative AI employs transformer models to process the context and generate outputs similar to humans.

Why Generative AI Is Driving New Opportunities

The increased interest in generative AI services is attributed to the potential they have to enable innovation and efficiency in multiple functions. 

Key Benefits of Generative AI 

1. Scalable Content Creation 

Generative AI enables businesses to generate large quantities of content within a short time, such as marketing content, reports, and product descriptions, thus helping them save time and be consistent in the content they generate.

2. Enhanced Customer Engagement 

Businesses can use AI-based chat tools to produce more personalized & engaging content, thus giving the customer a superior experience and effectively fulfilling their needs. 

3. Quicker Product Development 

Generative AI enables faster product development through the generation of prototypes, codes, and testing.

4. Personalization  

Businesses can use generative AI to create a personalized experience based on individual needs, hence providing a better experience and satisfying users.

5. Data Augmentation  

Artificial data helps improve models, especially when there is a lack of data, hence providing accurate results and improved performance.

These advantages make generative AI development a vital part of digital strategy for many organizations.

Real-World Applications Across Industries

Traditional AI Applications

• Financial fraud detection

• Predictive maintenance in manufacturing

• Inventory management

• Customer segmentation

Generative AI Applications

• AI chatbots and conversational agents

• Marketing content creation

• Code generation and debugging

• Image and video generation

• Virtual assistants and knowledge systems

These examples show how generative AI solutions extend beyond traditional automation into areas that require creativity and adaptability.

Combining Both Approaches

In fact, most organizations use a mix of traditional and generative AI to get the best out of the systems.

Example Use Case

• Traditional AI systems process customer information to find patterns

• Generative AI systems use the patterns to create personalized content

This enables businesses to use the advantages of both systems to create a more robust artificial intelligence system.

Challenges and Considerations

Traditional AI Limitations

• Limited flexibility

• Difficulty in dealing with unstructured data

• Need for manual updating for new applications

Generative AI Challenges

• Higher computational costs

• Chances of inaccurate or biased outcomes

• Need for effective governance and compliance

Understanding regulatory requirements is equally important, as generative AI regulatory compliance helps businesses manage risks effectively while adopting new technologies.

Business Impact of Generative AI

The rise of generative AI services is influencing how businesses operate and compete.

Key Areas of Impact

• Marketing and Content Creation

Faster production of high-quality content

• Customer Support

Improved interaction through intelligent chat systems

• Product Innovation

Rapid prototyping and idea generation

• Operational Efficiency

Automation of complex workflows

Organizations investing in generative ai development are finding new ways to improve productivity and deliver value.

Choosing the Right Approach

Selecting the right AI approach depends on the nature of your business needs.

Use Traditional AI When:

• Working with structured data

• Focusing on prediction and analysis

• Managing risk and compliance tasks

Use Generative AI When:

• Creating content or designs

• Building conversational interfaces

• Driving innovation and personalization

Key Factors to Consider

• Data availability and quality

• Infrastructure requirements

• Integration with existing systems

• Long-term scalability

A thoughtful evaluation helps businesses select the most suitable generative AI solution or combination of tools.

Concluding Thoughts

The difference between traditional AI and generative AI is based on how they approach problems and how they deliver the results. While traditional AI is based on analyzing data and making predictions, generative AI creates new information and allows for more interactive experiences.

As the business world continues to look into more sophisticated technology, the development of generative AI is a significant part of the current business plans. This is because it opens up more opportunities for creativity and efficiency in the customer world. 

However, traditional AI is still a viable tool in the world of data-based tasks. The most viable approach to artificial intelligence solutions is a mix of traditional and generative AI. This ensures a balance between traditional and generative artificial intelligence solutions and allows businesses to thrive and become more competitive in a more data-based world.