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자신 있는 분류 및 보고

AI 메시 기술을 활용한 우수한 정밀도로 광범위한 데이터 유형을 정확하고 효율적으로 분류합니다.

데이터 분류 소프트웨어

초고속으로 엔터프라이즈 전반의 데이터를 찾고 카탈로그화하세요.

Forcepoint Data Classification은 AI 메시를 사용하여 정확도 높은 데이터 분류 기능을 제공합니다. 네트워크화된 AI 아키텍처는 소규모 언어 모델과 고급 AI 구성 요소를 사용하여 효율성을 개선하고 거짓양성을 줄일 수 있습니다.

Forcepoint Data Classification을 선택해야 하는 이유

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AI를 사용하여 생성된 인사이트 정보는 데이터 분류에 대한 혁신적인 방식을 도출하므로 대규모 데이터를 어떻게 분류할 것인지 정확하고 효율적으로 결정할 수 있습니다.

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필요한 모든 곳의 데이터 보안

Forcepoint를 사용하여 어디에서나 데이터를 보호하세요. 당사의 데이터 보안 솔루션을 사용하면 침해를 방지하고 규정준수를 간소화하고 정책 관리를 통합하며 위험에 대응할 수 있습니다.

Frequently Asked Questions

What is data classification?

Data classification is the practice of tagging and organizing data into pre-defined categories, making it easier to locate and retrieve while enforcing secure access for authorized users.

Why is data classification important for organizations?

Data classification practices are necessary for maintaining a strong security posture. By ensuring that security teams know where to find sensitive information and by putting rules in place about who is allowed to access it, you can prevent or contain data breaches and keep unauthorized users away from resources they shouldn’t have.

What are the types of data classification?

Content-based classification: This is the practice of examining files and searching for sensitive information inside them. This can be helpful if you have a problem of information that is not for public consumption hiding in seemingly innocuous file types.

Context-based classification: Instead of examining file contents directly, this approach primarily looks at the metadata associated with files to find clues indicating that data inside is sensitive. This may include identifying the location where a file is saved, which user created it or which application the file is built for. This approach works well when your user base is well-trained and when you already have a degree of control over your sensitive data.

User-based classification: This puts the burden upon users to comb through files and categorize them. While at its best this approach can significantly cut down on false positives, it relies upon having not only a highly trained user base but also the time to manually classify data. That means that it is typically only suitable for a leaner organization or a smaller dataset..

What are best practices for implementing data classification?

Identification: Find where your sensitive data resides, including cloud repositories and physical hard drives, and take any necessary immediate steps to secure them with encryption, physical access controls, etc.

Organization: Come up with the process that you will use to organize data into categories.

Training: Empower employees to take a role in tagging data and placing it in the proper place based on its category. The more people who have a role in the process, the more stringent your training needs to be to make sure that human error doesn’t compromise your efforts.

Compliance: Understand the applicable data security and data privacy regulations for your operations, along with the penalties for noncompliance.

Solutions: Locate the data classification solution that best suits your organization. In many cases it can be best to utilize a comprehensive data security platform that can assist with data discovery, classification and prioritization instead of patching together different solutions from various vendors.

Can data classification help with regulatory compliance?

Yes, Forcepoint Data Classification can help streamline compliance in 80+ regions. It helps you avoid costly non-compliance fines with out-of-the-box key regulations and the broadest coverage of data types in the industry.

How accurate is Forcepoint Data Classification?

Forcepoint Data Classification uses AI-powered precision and efficiency. It helps classify data across multiple sources, using AI Mesh technology, to continuously improve its accuracy and efficiency, saving organizations valuable time and resources.

What is AI Mesh and how is it used?

Forcepoint Data Classification uses AI Mesh to deliver highly accurate data classification. Its networked AI architecture relies on a Small Language Model and advanced AI components to improve efficiency and reduce false positives.

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궁금하신 점은 분류 전문가가 답변해 드립니다.