In the ever-evolving digital landscape, the importance of data discovery and classification can’t be overstated. As we generate and interact with unprecedented volumes of data, the task of accurately identifying, categorizing, and utilizing this information becomes increasingly difficult.
This challenge is intensified by complex multi-cloud infrastructures and the swift proliferation of data. Shadow data, or misplaced and overlooked data, has emerged as a leading issue in cloud data security. In fact, 68% of security professionals identify shadow data as their primary challenge. This is further exacerbated by the employment of outdated processes or solutions that are ill-equipped to cater to the demands of present-day cloud data security. Traditional methods of data management are no longer sufficient for handling the vast and complex data landscape. Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively.
This is where artificial intelligence (AI) steps in, offering innovative solutions to enhance these processes. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks. Continuous monitoring and securing of data ensure that no shadow data is left exposed or unattended. AI-powered solutions are scalable, efficient, and capable of handling the complexities of today’s digital landscape. They offer a comprehensive solution to enhance your cloud security posture and effectively manage your data.
Understanding AI for data discovery
Data discovery is a process that involves understanding where data resides in your environment, including all public clouds, data warehouses, SaaS applications, cloud file shares, and on-prem storage. The primary focus of discovery is to find all the places where data exists and identify the assets it resides in. Data discovery and classification play a pivotal role in decision-making processes within organizations.
In the traditional approach, data discovery was a time-consuming and labor-intensive process. However, the advent of AI and machine learning (ML) has revolutionized this process. Generally, AI algorithms can sift through vast amounts of data at an astonishing speed, identifying patterns and correlations that would be impossible for humans to detect as rapidly and flawlessly. ML, a subset of AI, involves training models on existing data sets so they can make predictions or decisions without being explicitly programmed to do so. In the context of data discovery, ML algorithms can learn from past data exploration experiences to enhance future ones.
Laminar leverages AI analysis in its foperations to enhance and automate the process of data discovery. This advanced approach not only enhances the efficiency of detection models but also yields more insightful and valuable outcomes. By harnessing the power of AI, Laminar ensures a robust and comprehensive data discovery process, paving the way for informed decision-making and strategic insights. For a more in-depth understanding of how Laminar’s data discovery and classification tool works, read the data discovery and classification foundation article, a collaborative piece by cloud security experts.
Moreover, AI in collaboration with data discovery can significantly reduce public cloud data risks. By automating the process of finding and classifying sensitive information across various platforms and services, organizations can ensure that their data is adequately protected. It is also true of shadow data, which is old or redundant data that has been forgotten about but not discarded, and increases your company’s risk profile. By discovering and deleting redundant data, a business can reduce its attack surface, and thus the risk of data breaches.
This approach helps prevent unauthorized access and potential breaches, thereby enhancing the overall security posture of public cloud deployments.
Data discovery is an essential process in today’s data-driven world. The integration of AI and ML into this process has not only made it more efficient but also opened up new possibilities for extracting valuable insights from data. As we continue to generate and interact with increasing volumes of data, the role of AI in data discovery will only become more significant.
The power of AI and data classification
Data classification, often referred to as “tagging” or “labeling”, is a crucial process that categorizes data based on its type and sensitivity. It helps in determining what data you have and its sensitivity. To improve accuracy and eliminate false positives, intelligent validation methods may be employed to confirm classification. This process not only organizes data for efficient retrieval but also provides a comprehensive understanding of the data’s context, including its residency, owner, size, volume, and usage.
By employing intelligent validation methods to confirm classification and eliminate false positives, businesses can accurately assess their risk posture and make informed decisions about data security, compliance, and resource allocation. This understanding is crucial as it allows businesses to comprehend the potential impact of data disclosure and determine the applicable security controls.
AI has the power to make the data classification process more accurate and efficient. Machine learning algorithms can be trained to recognize patterns in the data and classify data accordingly. For example, an AI system could be trained to classify emails into categories like “sensitive” or “restricted” based on patterns it has learned from a training dataset. It could be further trained to classify data into important categories such as PII, PHI and PCI, increasing efficiency in both data classification, and ultimately security.
The power of AI in data classification lies in its ability to analyze large volumes of data quickly and accurately. As we continue to generate more and more data, the role of AI in data classification will only become more significant.
Leveraging AI for enhanced data discovery and classification
AI has the potential to revolutionize data discovery and classification by automating the process of identifying relevant data across diverse sources. It can rapidly analyze vast volumes of data, detecting patterns and correlations that would be challenging for humans to recognize. Similarly, AI can enhance data classification by automating the categorization of data into various types, classes, or categories based on learned patterns.
The integration of AI into these processes not only boosts efficiency but also paves the way for extracting valuable insights from data. For instance, AI can help uncover previously unnoticed patterns in the data, leading to new discoveries and insights inside of a business.
A key aspect of this process involves the use of specific algorithms and techniques. Machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, are commonly used in data classification. Natural language processing (NLP) is employed when working with text or speech data. Semantic annotation or context-based classification is used to attach additional information to various concepts relevant to the data.
AI and data discovery trends going forward
Looking ahead, we can anticipate several trends in the use of AI for data discovery and classification. One trend is the increasing use of deep learning algorithms for these processes. Deep learning, a subset of machine learning, involves training neural networks on large amounts of data and then using these networks to make predictions or decisions.
Another trend is the increasing integration of AI with other technologies such as cloud computing and big data analytics. This integration allows for more powerful and sophisticated data discovery and classification processes.
The intersection of AI, data discovery, and data classification represents a promising frontier as we continue to generate and interact with increasing volumes of data, the role of AI in these processes will only become more significant.
Navigating the challenges of AI in data discovery and classification
In the complex and dynamic landscape of data security, AI has emerged as a powerful tool for data discovery and classification. However, it’s not without its challenges. It’s crucial to understand these challenges to effectively leverage AI in our operations.
- One of the primary challenges is data quality. AI’s performance is heavily dependent on the quality of data it processes. Incomplete, inconsistent, or inaccurate data can lead to incorrect classifications, impacting the reliability of AI-driven insights.
- The complexity of data also poses a significant challenge. With the increasing volume and diversity of data, accurately classifying this information becomes increasingly difficult. This complexity can lead to misclassifications, affecting the overall effectiveness of AI in data discovery.
- Another critical challenge is bias and discrimination. AI systems can inadvertently perpetuate existing biases present in the training data, leading to discriminatory or biased outcomes. This can result in misclassifications or negative judgments that disproportionately affect certain demographics.
- Privacy concerns are also paramount when using AI for data discovery and classification. Handling sensitive data requires stringent measures to ensure privacy isn’t compromised. This is particularly relevant for data security professionals, where safeguarding privacy is a key part of their role.
- Finally, there are technical challenges associated with implementing AI solutions. These can require significant resources and expertise, which may not always be readily available.
Understanding these challenges allows us to better navigate the complexities of using AI in data discovery and classification. By focusing on improving data quality, mitigating biases, simplifying data where possible, respecting privacy regulations, and investing in necessary resources and expertise, we can effectively leverage AI to enhance our data security operations.
AI transforms data discovery and classification in the real world
There are numerous examples of how AI has transformed data discovery and classification across various industries, including e-commerce, healthcare, finance, and manufacturing. Laminar, a leading AI-driven data discovery and classification tool, exemplifies this transformation.
Empowering e-commerce with AI-driven data discovery and classification
In the rapidly evolving e-commerce landscape, agility is key. Payability, a leading funding platform for e-commerce businesses, understands this well. The company provides sellers with immediate access to marketplace payouts, enabling them to quickly replenish inventory, launch new products, and negotiate better deals with suppliers. However, maintaining this level of agility requires robust data governance processes, particularly in the cloud.
Enter Laminar, an agile data security platform that leverages AI for data discovery and classification. By implementing Laminar, Payability has gained continuous visibility of its cloud data without the need for manual input. The platform autonomously discovers where data is stored, and identifies data type, who has access to it, how and where it’s used, and how it flows through the cloud environment.
Laminar’s AI-driven approach helped Payability identify redundant, obsolete, or trivial (ROT) data that could then be safely archived or deleted. This not only reduced cloud costs but also minimized the risk of relying on outdated or irrelevant data during the software development process.
The combination of AI with data discovery and classification has empowered Payability to improve its data governance and security posture while preserving business agility.
Harnessing AI for enhanced data discovery and classification at WalkMe
WalkMe, a leading player in the SaaS industry, provides a cloud-based digital adoption platform that helps organizations accelerate their digital transformations. As the company grew, it faced the challenge of securing a variety of cloud assets and ensuring the security of customer data across AWS, Azure, and GCP platforms.
The challenge was twofold: understanding the type of data they had in each asset and ensuring that their security controls in the cloud grew in tandem with their platform. WalkMe needed a solution that could keep pace with its growth and provide comprehensive visibility into its data landscape.
Laminar’s AI capabilities enabled WalkMe to discover and classify the sensitivity of their data, providing a clear picture of where it resided and what their exposure was. It also identified misplaced sensitive data, such as personal information in lower environments or geographies requiring strict regulatory control, or publicly exposed sensitive information.
This AI-driven approach empowered WalkMe to proactively secure their customer data and ensure robust data security controls as they continued to grow. The result was a more secure platform for their customers and a stronger foundation for WalkMe’s ongoing digital transformation journey.
AI offers innovative solutions for data discovery and classification in today’s complex and dynamic data landscape. By automating processes and enhancing accuracy, AI can transform these processes and facilitate decision-making. However, leveraging AI for data discovery and classification requires a strategic approach. Organizations need to understand their data landscape, define their security needs, and choose the right AI tools and techniques.
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