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Sentra Inc., a cloud-native security startup, has announced a new feature for its platform: Sentra Jagger, an AI assistant powered by a large language model (LLM). Sentra Jagger aims to provide real-time security insights and recommendations for organizations using cloud data.
What is Sentra Jagger?
It is a new capability that will be added to Sentra’s existing products: Data Detection and Response (DDR) and Data Security Posture Management (DSPM). These products help security teams gain full visibility and control of cloud data and protect against various security issues, such as data duplication, over-permissions, misconfigurations, and unauthorized access.
Sentra Jagger will enhance the functionality of these products by offering an AI-powered assistant that can analyze and respond to cybersecurity threats. Sentra Jagger will be available for a limited preview in March and will be generally available in the second half of 2024.
How does Sentra Jagger work?
Sentra Jagger works by seamlessly embedding within the product interface, allowing users to interact with it through natural language. Users can ask questions, request reports, and get recommendations from Sentra Jagger, without having to navigate through complex portals or dashboards.
Sentra Jagger also provides simplified interpretations of security queries, making it easy for users of different levels of expertise to understand the results. For example, users can ask questions like “How many data stores are exposed to the public?” or “What are the best practices for securing my data?” and get clear and concise answers from Sentra Jagger.
Sentra Jagger also integrates with the existing tech stack, giving users a unified security management experience and a holistic view of the organization’s data security posture.
What are the benefits?
According to Sentra, Sentra Jagger can reduce the time required to accomplish tasks such as data store reporting and policy implementation by as much as 80%. This means that security teams can improve their operational efficiency and strengthen their security measures.
It also helps security teams cope with the increasing demand for data protection in the cloud-native environment. Garner predicts that by 2025, over 95 percent of new digital workloads will be deployed on cloud-native platforms, up from 30 percent in 2021. This means that cloud data security will become more critical and challenging than ever. It provides an innovative solution for this challenge, using LLMs to make cloud data security more accessible and effective.
How does Sentra classify sensitive data?
Sentra classifies sensitive data using a mix of regular expressions, list classifiers, validation functions, and large language models (LLMs). LLMs are a type of artificial intelligence that can comprehend natural language and produce text. Sentra classifies unstructured data, such as paragraphs of text, using LLMs that analyze the data’s context and meaning.
Sentra, for example, can use LLMs to identify and label documents based on whether they are legal contracts, payslips, or technical documentation. Sentra can also use LLMs to identify and mark sensitive entities in unstructured data, such as names, addresses, and phone numbers. Sentra uses LLMs to classify sensitive data more accurately and efficiently than traditional methods.
How does Sentra protect against data duplication?
Sentra prevents data duplication by detecting and removing critical shadow data that is generated but not used or monitored in the cloud environment. If not managed properly, shadow data can pose security risks as well as increase costs.
Sentra’s Data Security Posture Management (DSPM) platform identifies and classifies sensitive data, including PCI, PII, PHI, and source code, and assists security teams in deleting or securing it. By eliminating data duplication, Sentra reduces the data attack surface and improves the organization’s data security posture.
