For many machine learning teams, the primary bottleneck in artificial intelligence development is no longer compute or data, but the human capacity to create and test new ideas at scale. With more than 2,000 machine learning papers published every week, no human research team can effectively evaluate and implement every new breakthrough while advancing their own original hypotheses. Autoscience addresses this problem using two core AI systems: automated scientists that ideate and test new algorithmic hypotheses and automated engineers that optimize and deploy those validated inventions into the real world. Autoscience’s first deployments target high-stakes financial applications, manufacturing and fraud detection, enabling companies to benefit from the output of a fully-staffed research division without the headcount.

Autoscience first gained recognition when its autonomous lab became the first AI system to produce a peer-reviewed scientific research paper (ICLR 2025 workshop). Soon after, its system secured a Silver Medal in a machine learning competition (Kaggle Santa 2025) against 3,300 teams, marking the first time a fully-autonomous system has placed in a live, featured Kaggle competition.

Also Read: Databricks Signs Definitive Agreement to Acquire MosaicML, a Leading Generative AI Platform 

“We’ve reached a point where human intuition is no longer enough to navigate the complexity of algorithmic discovery,” said Eliot Cowan, CEO of Autoscience. “We’ve built a research organization where the researchers are AI systems. We aim to compress a decade of machine learning research into months, unlocking new AI capabilities for scientists and forming a competitive edge for our customers.”

“We believe Autoscience is tackling an increasingly important challenge in machine learning: the pace and scalability of experimentation,” said Yuri Sagalov, Managing Director at General Catalyst. “As research output continues to grow, teams are looking for ways to more efficiently test, validate, and translate new ideas into production systems. We’re excited about their progress in advancing autonomous R&D to scale that workflow.”

The $14 million in funding will be used to scale Autoscience’s offering to a select group of Fortune 500 and large private companies who are training specialized models in high-stakes environments. This managed service deploys hundreds of automated AI Research Scientists that continuously generate and ship improvements to their machine learning models at the same time, enabling companies to discover, test, and serve better models. The capital will also support the expansion of Autoscience’s engineering team as they accelerate AI research.

Source: BusinessWire

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Also Read: Google Unveils T5Gemma 2: Advanced Multimodal Encoder-Decoder Model for AI Developers 

They can also explore deal information and access links to original sources for deeper analysis. “Together, PitchBook and Perplexity are expanding access to private market intelligence,” said Tom Van Buskirk, EVP of Technology & Engineering at PitchBook. “AI is most effective when it’s powered by the most accurate, high-quality data. Perplexity’s conversational interface opens new ways to explore information and pairing it with PitchBook’s insights helps professionals find the clarity they need to make confident decisions.” The integration also reflects PitchBook’s broader strategy to expand its AI partnership ecosystem.

Read More: PitchBook Announces New Essential MCP Integration with Perplexity, Expanding Access to AI-Powered, Verifiable Market Intelligence

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This partnership will enable the development of human-like digital identities, called “ATMANS,” which mimic the appearance, voice, and personality of real individuals. These AI-driven digital humans are designed to communicate naturally with users, delivering information in multiple languages while maintaining the nuances of the original human identity. By combining Brahma AI’s platform with Google Cloud technologies such as the Veo generative video model and Gemini multimodal AI, enterprises can deploy highly personalized and interactive experiences at global scale.

According to the companies, the platform allows organizations to build movie-quality digital humans capable of engaging audiences through natural conversation and audiovisual interaction. These digital agents can find applications in various industries, such as healthcare, media and entertainment, retail, and sports, and help businesses provide personalized communication and services around the clock.

For example, in the healthcare industry, hospitals can utilize digital human technology to help patients understand various medical processes, symptoms, and healing processes in their native language. Similarly, in the sports industry, various sports organizations can utilize this technology to allow their players to communicate with their fans using their digital human avatars. Moreover, in the media and entertainment industry, various media organizations can utilize this technology to reach a wider audience without compromising their authenticity.

Also Read: SEI and IBM Partner to Accelerate Enterprise Transformation with Agentic AI 

Security and governance is another critical aspect of this initiative. Brahma AI has integrated a consent-driven creation and digital watermarking system to ensure that users are in total control of their digital human avatars. This is particularly critical in addressing concerns over synthetic media, identity, and AI governance.

Implications for the IT Industry

The partnership is indicative of the increasing rate of change in the tech sector, particularly in terms of the adoption of generative AI and immersive digital technologies. Digital humans are a product of several emerging technologies, which include AI-generated videos, natural language processing, cloud computing, and real-time data processing.

The development is particularly significant for IT service providers and tech companies, particularly in terms of an increasing need for digital infrastructure that is capable of supporting high-performance generative AI applications. Cloud computing platforms like Google Cloud are at the center of this development, particularly in terms of scalable computing resources, AI technologies, and security frameworks that are critical in the deployment of digital humans.

The development is also indicative of a larger trend in terms of AI-based enterprise platforms, in which artificial intelligence is integrated into business applications. As digital humans are integrated into customer engagement, training, and knowledge-sharing applications, there is a need for seamless integration with existing enterprise platforms.

Broader Business Impact

Outside of the tech industry, digital human technology has the potential to change the way businesses interact with customers, employees, and partners. Digital humans can be leveraged by businesses as a tool to offer customized communication experiences.

For multinational companies, this has huge implications in terms of saving on the costs and hassles of content creation, customer service, and training. Instead of developing content for various languages and regions, companies can utilize digital humans that can change their communication style according to the region.

Digital humans can also be used as a means to increase customer engagement, as they can be made more human and natural through AI. This has huge implications for businesses in industries where trust is paramount, such as healthcare, finance, and retail.

A Glimpse into the Future of Human-AI Interaction

The Brahma AI and Google Cloud deal signals the rising trend of human-centric AI experiences, in which technology enables, instead of replacing, human communication. This creates the possibility of creating digital identities with the power of generative AI and digital human representation, yet retaining authenticity.

With more and more businesses investing in AI-powered engagement technologies, such deals are expected to further fuel the pace of innovation in the IT space. For businesses looking to build more immersive digital experiences and operate on a global scale, interactive digital humans could be the next big thing in the evolution of enterprise technology.

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In Hardware & Networks news this week…

Qvantel Completes Acquisition of Optiva, Creating a Global Leader in AI-Powered Telecom Monetization

Qvantel finalizes its acquisition of Optiva, forming a global powerhouse in AI-driven telecom monetization and digital operations. The move enhances real-time charging, BSS modernization, and intelligent revenue management for telecom operators worldwide.

In Business Technology news this week…

Avaya Expands AI-First Workplace Strategy with Gemini Enterprise and Google Workspace

Avaya advances its AI-first workplace vision by integrating Gemini Enterprise with Google Workspace. The expansion enables smarter collaboration, automated workflows, and enhanced employee productivity across hybrid enterprise environments.

In FinTech news this week…

Accenture Acquires Cabel Industry to Boost FinTech Modernization

Accenture acquires Cabel Industry to strengthen its fintech transformation capabilities. The acquisition enhances Accenture’s ability to deliver digital banking modernization, regulatory compliance, and scalable financial platforms to global financial institutions.

In HealthTech news this week…

Equanimity AI to Debut BEACON, an AI-Operated Pain OS, at NANS 2026

Equanimity AI announces the debut of BEACON, an AI-powered pain operating system, at NANS 2026. The platform leverages advanced analytics and machine learning to support precision pain management and improved clinical outcomes.

In Artificial Intelligence news this week…

Nota AI Selected to Optimize Samsung’s Exynos 2600, Expanding On-Device AI Innovation

Nota AI is selected to optimize Samsung’s Exynos 2600 chipset, strengthening on-device AI performance and energy efficiency. The collaboration advances edge AI capabilities across next-generation consumer and enterprise devices.

In Machine Learning news this week…

Google Unveils T5Gemma 2, an Advanced Multimodal Encoder-Decoder Model for AI Developers

Google introduces T5Gemma 2, a powerful multimodal encoder-decoder model designed to support advanced AI development. The release enables developers to build more capable applications across text, vision, and multimodal reasoning tasks.

In Quantum Computing news this week…

IonQ and KISTI Advance South Korea’s Quantum Ambitions with 100-Qubit System Deployment

IonQ and KISTI collaborate to deploy a 100-qubit quantum system, accelerating South Korea’s national quantum computing roadmap. The initiative strengthens research capabilities and lays the foundation for next-generation computational breakthroughs.

Article of the Week

Cloud-Native Applications for the Enterprise: How Organizations Build Scalable, Resilient Digital Platforms

Cloud-Native Applications
This featured analysis explores how enterprises are adopting cloud-native architectures to improve scalability, resilience, and agility. The article outlines best practices for containerization, microservices, and platform engineering to support long-term digital transformation.

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Also Read: IonQ and KISTI Advance South Korea’s Quantum Ambitions With 100-Qubit System Deployment

T5Gemma 2 also offers ultra-long context handling, with support for sequences up to 128,000 tokens, and robust multilingual understanding across more than 140 languages, inheriting the advanced features of Gemma 3 while outperforming its predecessors on long‐context and multimodal benchmarks. Pre-trained checkpoints are now available to developers, who can further post-train the models for specific applications, underscoring Google’s commitment to open, efficient AI innovation.

Read More: T5Gemma 2: The next generation of encoder-decoder models

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What is Machine Learning?

Machine learning, a facet of artificial intelligence (AI), mimics human learning by utilizing data and algorithms. It involves training machines to autonomously learn and enhance accuracy over time without explicit programming. By identifying patterns and making predictions, machine learning empowers computers to execute tasks and decisions independently. With applications ranging from recommendation systems to self-driving cars, machine learning is a transformative force, solving intricate problems and leaving a substantial impact on how we live and work.

Examples of machine learning include recommendation systems in streaming services, fraud detection in finance, personalized medical treatments, autonomous vehicles’ decision-making, and natural language processing for voice assistants.

Applications of Machine Learning

Machine learning applications are diverse and impactful, shaping various sectors. Below are a few of them:

• Spam Detection: Email providers utilize machine learning algorithms to identify and filter spam messages from users’ inboxes.
• Social Media Features: Social media platforms employ machine learning to suggest friends, pages, and content based on users’ activities and preferences.
• Product Recommendations: E-commerce companies leverage machine learning for personalized product recommendations, enhancing the shopping experience.
• Image Recognition: Machine learning is applied in image recognition tasks, identifying objects, persons, and places in digital images.
• Self-Driving Cars: Machine learning plays a crucial role in enabling autonomous driving by analyzing sensor data and making real-time decisions.
• Medical Diagnosis: Healthcare utilizes machine learning models for tasks like breast cancer classification, Parkinson’s disease detection, and pneumonia diagnosis.

Top 5 Machine Learning Use Cases

Now that we have a fair idea about what machine learning is, let’s understand the top 5 machine learning use cases.

Machine Learning in Finance: Fraud Detection for Secure Transactions

In the realm of finance, machine learning proves invaluable, particularly in fraud detection. With banks and financial institutions spending $2.92 to recover every $1 lost to fraud, ML techniques play a crucial role in enhancing transactional security. Applications like credit card fraud detection, utilizing deep learning solutions in Python or R programming, predict and identify fraudulent behavior in real-time, generating alerts promptly. Notably, CitiBank employs Feezai’s anomaly detection system across 90 countries, utilizing AI and ML to bolster payment monitoring and security, effectively mitigating fraud risks.

Machine Learning Use Cases in Healthcare

Machine learning in healthcare has revolutionized pattern recognition, particularly in radiology imaging, where AI-enabled computer vision is utilized for mammogram analysis and early lung cancer screening. Addressing the 40% miss rate in breast cancer detection, ML improves diagnostic accuracy. ML is also applied to classify tumors, identify subtle bone fractures, and detect neurological disorders. In inpatient care, ML examines historical records to create personalized treatment plans. In genetic research, ML identifies genetic markers, enabling personalized medication recommendations. Furthermore, ML accelerates drug discovery. For instance, Pfizer utilizes IBM Watson to select optimal candidates for clinical trials. Meanwhile, Geisinger Health System uses AI and ML to predict sepsis risk, enhancing patient prioritization and reducing mortality rates.

Machine Learning and Smartphones

ML plays a significant role in our smartphones, influencing key features we use daily. Facial recognition, governed by ML algorithms, unlocks our phones, while voice assistants like Siri, Alexa, Google Assistant, and Cortana use ML and Natural Language Processing (NLP) to understand and respond to our commands.

Also Read: Spatial Computing: Everything You Need to Know About this Next Frontier in Digital World 

Moreover, machine learning use cases in smartphones also extend to battery optimization, where ML algorithms learn a user’s usage patterns to adjust power consumption accordingly. It analyzes and improves photos through image classifiers, identifies objects or faces in images, and even employs artificial neural networks to enhance or extend photos by predicting what lies beyond their borders.

Revolutionizing Transportation with Machine Learning

Machine learning (ML) plays a pivotal role in modern transportation. Google Maps utilizes ML algorithms to assess current traffic conditions, determine optimal routes, suggest nearby places to explore, and estimate arrival times.

Ride-sharing giants like Uber and Lyft leverage ML for tasks such as matching riders and drivers, pricing, traffic analysis, and route optimization based on real-time conditions, similar to Google Maps.

Self-driving cars are empowered by computer vision and are driven by unsupervised ML algorithms. These algorithms enable cars to gather data from cameras and sensors, comprehend their surroundings, and make real-time decisions to navigate effectively. Machine learning use cases in the transportation sector are steering the future toward efficiency and safety.

Email Monitoring and Cybersecurity

Emails, crucial for personal and professional communication, are vulnerable to cybersecurity threats like phishing. Machine learning (ML) techniques, particularly Natural Language Processing, play a pivotal role in real-time email monitoring to detect and prevent attacks. Anomaly detection using machine learning use cases is effective in identifying suspicious activities.

Tessian, a London-based software company, exemplifies this approach by employing ML-based email monitoring software. This technology combines NLP and anomaly detection to proactively prevent phishing attacks, information breaches, and malware incidents, ensuring robust cybersecurity for email communications.

Final Verdict

Machine learning use cases across industries showcase its transformative impact. From enhancing cybersecurity and optimizing financial processes to revolutionizing healthcare diagnostics and powering self-driving cars, the versatility of machine learning continues to reshape and elevate various facets of our technological landscape.

The post Top 5 Machine Learning Use Cases In 2024 appeared first on ITDigest.

Without further ado, let’s dive in and learn all about it.

What is a Machine Learning Algorithm?

Algorithms for machine learning (ML) are computational processes or methods that let computers or other devices learn from information, detect trends, forecast the future, or act on it without having to be explicitly programmed.

These algorithms are a key element of machine learning, a subfield of computer science and artificial intelligence (AI).

For the purpose of gaining insightful knowledge, identifying patterns, and formulating predictions or judgments, ML algorithms evaluate and process enormous volumes of data. These include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning.

Recently, Netflix was able to save $1 billion using its machine learning algorithm for personalization and content recommendations.

With the machine learning algorithms explained, now let’s explore its types.

What are the types of Machine Learning Algorithms?

Using labeled training data, where the input data and appropriate output labels are provided, supervised learning algorithms can learn new information. These algorithms process the labeled data to correctly forecast or categorize brand-new, untainted data.

• Unsupervised learning algorithms examine unlabeled data to identify hidden relationships, structures, or patterns without the use of fixed output labels. These methods aid in the identification of data anomalies, correlations, or clusters. Unsupervised learning is further classified into two types: clustering and association.
• Supervised learning methods are an ML algorithm wherein the system requires external supervision in order to learn. The dataset that is labeled is extensively used to train the supervised learning models. After training and processing, the model is put to the test by being given a sample set of test data to see if it can accurately predict the desired result. Supervised learning is further classified on the basis of the problem into Classification and Regression.
• Reinforcement learning methods train an agent to respond in a way that will maximize rewards or lower punishments. The algorithm gains knowledge by making mistakes and then learning from them by getting praise or incentives.
• Deep learning algorithms are a category of ML algorithms that learn and extract complicated patterns or representations from data using artificial neural networks with numerous layers. When it comes to tasks like speech recognition, natural language processing, and image recognition, these algorithms have proved to be surprisingly effective.

ML algorithms are the fundamental building blocks that allow computers to learn from data and make defensible judgments or predictions. They have uses in a variety of industries, from marketing and self-driving cars to healthcare and finance.

How Do Machine Learning Algorithms Work?

When drawing predictions, classifications, or taking actions, machine learning algorithms use data to identify patterns and relationships. The algorithm’s specific operations vary depending on its nature and the problem it attempts to answer. According to Nature, ML algorithms were used to predict the mortality of COVID-19 patients with 92% accuracy. Breast cancer can be identified with 99% accuracy by Google’s Deep Learning ML software.

Here’s the complete machine learning algorithms cheat sheet you need:

1. Data Gathering: For the machine learning activity, relevant information is gathered and prepared. For supervised learning, these data may consist of features (input variables) and labels (output variables), or unlabeled data for unsupervised learning.
2. Data Preprocessing: The gathered data is cleaned, converted, and preprocessed to guarantee its quality and compliance with the algorithm. This stage might involve deleting duplicates, dealing with missing values, scalability of features, or categorical variable encoding.
3. Training Phase: In supervised learning, the algorithm learns to map the input features to the matching output labels by training on labeled data. Utilizing optimization techniques, the program modifies its internal parameters or model during training to reduce the discrepancy between expected and real labels.
4. Evaluation: Post training, the algorithm is assessed using a different dataset known as the test set. This assessment gauges how well the algorithm performs and how well it can categorize or make predictions based on brand-new data.
5. Model Deployment: If the algorithm performs satisfactorily, it can be applied to new, real-world data to make predictions or assign categories. This can entail incorporating the model into a program or system so that it can communicate with users or offer information.
6. Iterative Improvement: ML algorithms can be improved regularly by adding more data, retraining the model, adjusting hyperparameters, or utilizing more complex methods like deep learning or ensemble learning.

It’s crucial to remember that various machine learning techniques, including decision trees, support vector machines, neural networks, and clustering algorithms, each have unique underlying principles and algorithms. Depending on the characteristics of the problem at hand and the data at hand, each algorithm has its advantages, disadvantages, and best uses.

Additionally, the suitability and representativeness of the training data, the selection of relevant features, and the careful choice of algorithmic parameters are all pivotal for the success of ML algorithms.

Winding Up

The way we tackle complicated problems and make predictions across a variety of fields has been completely transformed by machine learning algorithms. These algorithms are able to spot patterns, learn from data, and generate precise predictions or classifications. The performance of ML algorithms can be enhanced continuously through the iterative process of training, evaluating, and refining.

Many industries, such as healthcare, finance, marketing, and robotics, have effectively used ML algorithms. They have the ability to boost decision-making processes, automate procedures, and extract hidden insights from massive datasets. ML algorithms have the potential to revolutionize industries and enhance our daily lives by doing everything from forecasting the impact of diseases to making personalized product recommendations.

That being said, it’s crucial to understand that ML algorithms are not flawless. They are only as good as the training data and feature sets that they are fed. Predictions may be inaccurate or prejudiced if the training data contains biases or mistakes. Therefore, to guarantee the accuracy and fairness of machine learning algorithms, thorough data collection, preprocessing, and model validation are essential.

The post Harnessing the Power of Machine Learning Algorithms: What You Need to Know appeared first on ITDigest.

MosaicML is known for its state-of-the-art MPT large language models (LLMs). With over 3.3 million downloads of MPT-7B and the recent release of MPT-30B, MosaicML has showcased how organizations can quickly build and train their own state-of-the-art models using their data in a cost-effective way. Customers such as AI2 (Allen Institute for AI), Generally Intelligent, Hippocratic AI, Replit and Scatter Labs leverage MosaicML for a wide variety of generative AI use cases.

“Every organization should be able to benefit from the AI revolution with more control over how their data is used. Databricks and MosaicML have an incredible opportunity to democratize AI and make the Lakehouse the best place to build generative AI and LLMs,” said Ali Ghodsi, Co-Founder and CEO, Databricks. “Databricks and MosaicML’s shared vision, rooted in transparency and a history of open source contributions, will deliver value to our customers as they navigate the biggest computing revolution of our time.”

Also Read: Appy Pie Announces Alpha Launch of AppyLM: Revolutionary Large Language Model for Native Mobile App Code Generation 

Giving Organizations a Simple, Fast Way to Build, Own and Secure Models

Virtually every organization is exploring how best to use generative AI and LLMs, and every leader is considering how they leverage these new innovations while retaining control of their most precious resource: their data. Organizations and executives want to be able to build, own and secure their own models.

The Databricks Lakehouse Platform, combined with MosaicML’s technology, will offer customers a simple, fast way to retain control, security, and ownership over their valuable data without high costs. According to MosaicML, automatic optimization of model training provides 2x-7x faster training compared to standard approaches. Combined with near linear scaling of resources, multi-billion-parameter models can be trained in hours, not days. With Databricks and MosaicML, training and using LLMs will cost thousands of dollars, not millions.

Databricks’ unified Data and AI platform combined with MosaicML’s generative AI training capabilities will provide a platform robust enough to serve the world’s largest organizations and flexible enough to address a broad range of AI use cases.

SOURCE: PRNewswire

The post Databricks Signs Definitive Agreement to Acquire MosaicML, a Leading Generative AI Platform appeared first on ITDigest.

With the NVIDIA NeMo platform for developing large language models (LLMs) and NVIDIA GPU-accelerated computing, Snowflake will enable enterprises to use data in their Snowflake accounts to make custom LLMs for advanced generative AI services, including chatbots, search and summarization. The ability to customize LLMs without moving data enables proprietary information to remain fully secured and governed within the Snowflake platform.

“Snowflake’s partnership with NVIDIA will bring high performance machine learning and artificial intelligence to our vast volumes of proprietary and structured enterprise data, a new frontier to bringing unprecedented insights, predictions and prescriptions to the global world of business,” said Frank Slootman, chairman and CEO, Snowflake.

Also Read: Mendix Adds Powerful New AI and Machine Learning Capabilities to its Market and Technology-Leading Enterprise Low-Code Platform 

“Data is essential to creating generative AI applications that understand the complex operations and unique voice of every company,” said Jensen Huang, founder and CEO, NVIDIA. “Together, NVIDIA and Snowflake will create an AI factory that helps enterprises turn their own valuable data into custom generative AI models to power groundbreaking new applications — right from the cloud platform that they use to run their businesses.”

NVIDIA and Snowflake’s collaboration represents a new opportunity for enterprises. It will enable them to use their proprietary data — which can range from hundreds of terabytes to petabytes of raw and curated business information — to create and fine-tune custom LLMs that power business-specific applications and services.

By integrating AI technology from Snowflake and NVIDIA, customers can quickly and easily build, deploy and manage customized applications that bring the power of generative AI to all parts of their business across a variety of use cases. In addition, expanding AI capabilities in the Data Cloud enables these customers to create generative AI applications where their governed data already resides, a benefit that significantly reduces cost and latency while maintaining the security of their data.

“More enterprises than we expected are training or at least fine-tuning their own AI models, as they increasingly appreciate the value of their own data assets,” said Alexander Harrowell, principal analyst for advanced computing for AI at technology research group Omdia. “Similarly, enterprises are beginning to operate more diverse fleets of AI models for business-specific applications. Supporting them in this trend is one of the biggest open opportunities in the sector.”

SOURCE: Businesswire

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Generative AI and large language models (LLMs) have the potential to revolutionize enterprise operations and customer engagements, but the privacy and compliance risks associated with the presence of personal, private, and confidential information in training data and subsequent models have enterprises scrambling to ensure proper security and access controls are in place.

With native enforcement of security and privacy controls across diverse data estates, and architectures, and built on open standards, Privacera’s latest innovation helps companies reduce sensitive data exposure, increase privacy and ethics, and address regulatory and legal compliance issues with AI.

Also Read: Mendix Adds Powerful New AI and Machine Learning Capabilities to its Market and Technology-Leading Enterprise Low-Code Platform 

PAIG fosters powerful AI data security governance and federated stewardship between IT departments and business teams. The solution brings together comprehensive data security governance for relational data, non-structured data as well as AI model training and access. PAIG combats the unpredictability of generative AI by helping companies avoid the potential misuse of data, address challenges in compliance policy enforcement, and reduce complexities that arise when runtime contexts are added during inference.

With PAIG, organizations can tap into Privacera‘s proven history of innovation in building massively scalable data and access security on AI and diverse data estates. PAIG is powered by the company’s Unified Data Security Platform, which has set the standard for data in the big data ecosystem and the modern cloud data estate. This allows for a common security administration and monitoring platform across all data, along with consistent policies, roles, and controls across all AI models.

The combined solution provides compliance support for CCPA, GDPR, and HIPAA during the training, deployment, and utilization of AI models.

“The potential of generative AI and large language models (LLMs) to transform enterprise operations is immense, but their inherent unpredictability can unknowingly reveal intellectual property, Personally Identifiable Information (PII) and sensitive data,” said Privacera co-founder and CEO Balaji Ganesan. “By providing organizations with an intelligent and adaptive AI data governance solution, Privacera continues its mission to empower enterprises to utilize their data as a strategic asset.”

SOURCE: PRNewswire

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