登录查看更多内容

How can you secure an expert system's data?

由人工智能和领英社区提供技术支持

Expert systems are computer programs that use data and rules to mimic human expertise in a specific domain, such as medical diagnosis, financial analysis, or legal reasoning. They are often used to support decision-making, provide recommendations, or automate tasks. However, to ensure their reliability, accuracy, and security, you need to protect their data from unauthorized access, modification, or deletion. In this article, we will explore some of the methods and challenges of securing an expert system's data.

在这篇协作文章中查找专家回答

由社区从 5 条内容中精选。了解更多

1 Data encryption

One of the most common and effective ways to secure an expert system's data is to encrypt it. Encryption is the process of transforming data into a coded form that can only be read by authorized parties who have the key to decrypt it. Encryption can protect data in transit, such as when it is sent over a network, or in storage, such as when it is saved on a disk or a cloud service. Encryption can also prevent data leakage, tampering, or theft by hackers, insiders, or competitors.

添加您的观点

Milan Prajapati

Microsoft Certified: Data Analyst | Microsoft Certified: Data Engineer | Data Scientist | Machine Learning Engineer | Cloud Engineer
举报内容
Beyond implementing standard encryption measures, when leveraging cloud services such as AWS, GCP, and Azure, these platforms provide specialized services tailored for encrypting Personally Identifiable Information (PII). These services offer a user-friendly approach to data encryption, allowing you to secure sensitive information without the need for in-depth technical expertise in encryption and decryption procedures. By incorporating these cloud-based encryption solutions into your data protection strategy, you can enhance the security of personal identifying information, ensuring a comprehensive and accessible approach to safeguarding sensitive data.

已翻译

赞

2 Data anonymization

Another way to secure an expert system's data is to anonymize it. Anonymization is the process of removing or masking any identifying or sensitive information from data, such as names, addresses, phone numbers, or social security numbers. Anonymization can help protect the privacy and confidentiality of the data subjects, such as customers, patients, or employees, who may not want their personal or behavioral data to be exposed or misused. Anonymization can also reduce the risk of legal or ethical issues, such as compliance with data protection laws or consent requirements.

添加您的观点

3 Data integrity

A third way to secure an expert system's data is to ensure its integrity. Integrity is the property of data that guarantees its consistency, accuracy, and validity. Integrity can be compromised by errors, bugs, or malicious attacks that can corrupt, alter, or delete data. To ensure data integrity, you need to implement quality control measures, such as validation, verification, or auditing, that can detect and correct any data anomalies or discrepancies. You also need to backup and restore your data regularly, in case of data loss or damage.

添加您的观点

Milan Prajapati

Microsoft Certified: Data Analyst | Microsoft Certified: Data Engineer | Data Scientist | Machine Learning Engineer | Cloud Engineer
举报内容
As your mentor, I'd like to encourage eveyone to not only consider reactive measures such as validation, verification, and auditing but also adopt proactive strategies. Embrace secure coding practices, stay updated on security patches, and make these a consistent part of your development approach. This not only prevents potential issues but also strengthens the overall security of your system. Additionally, your mention of regular backups is key. Having a comprehensive backup and restoration strategy serves as a safety net, offering a means to recover data in unforeseen circumstances.

已翻译

赞

4 Data access control

A fourth way to secure an expert system's data is to control its access. Access control is the process of granting or denying permissions to access data based on predefined rules, roles, or policies. Access control can help prevent unauthorized or inappropriate use of data by limiting who, what, when, where, and how data can be accessed. Access control can also enforce accountability and traceability of data usage by logging and monitoring data activities and transactions.

添加您的观点

Milan Prajapati

Microsoft Certified: Data Analyst | Microsoft Certified: Data Engineer | Data Scientist | Machine Learning Engineer | Cloud Engineer
举报内容
I believe that when processing data and authorizing access for others, it's essential to grant access only to those who genuinely need it. Specifically, when dealing with Personally Identifiable Information (PII), it's crucial to conceal this sensitive information. Implementing access logs is equally important—tracking and noting the type of information each person accessed enhances security. Furthermore, if you're utilizing cloud services, consider enabling cloud monitoring tools such as CloudWatch to keep a close eye on access details. This allows you to identify who accessed your data and the specific information they interacted with, providing an additional layer of security and accountability.

已翻译

赞

5 Data security challenges

Securing an expert system's data is not a trivial task. It involves balancing trade-offs between security and performance, usability, and scalability. It also requires addressing technical and organizational challenges, such as choosing the right encryption algorithms, anonymization techniques, integrity methods, and access control models; managing the complexity and diversity of data sources, formats, and types; ensuring the compatibility and interoperability of data systems and platforms; and coordinating the roles and responsibilities of data owners, users, and providers.

添加您的观点

Milan Prajapati

Microsoft Certified: Data Analyst | Microsoft Certified: Data Engineer | Data Scientist | Machine Learning Engineer | Cloud Engineer
举报内容
Securing data in expert systems demands a delicate balance between security, performance, and usability. Addressing challenges involves strategic decisions on encryption, anonymization, integrity, and access control. Managing diverse data sources and ensuring compatibility across platforms is pivotal. Coordination among stakeholders establishes clear accountability. A holistic approach, balancing security with usability, fortifies expert systems against evolving threats.

已翻译

赞

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Shantanu Sharma, Ph.D.

ML, SaaS · GS, GE Research · UNC, IITK · NMLS # 1677482
举报内容
Securing expert system data goes beyond basics like encryption. Explore advanced techniques like homomorphic encryption, differential privacy, and secure multi-party computation for stronger confidentiality and privacy. Tackle challenges like complexity, evolving threats, and compliance with data security tools like Secure Multi-Party Computation (SMPC), federated learning, and blockchain. Remember, user education and continuous assessments are crucial. #SecureAI #ExpertSystems

已翻译

赞

Data Mining

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can you secure an expert system's data?

1

2

3

4

5

6

1 Data encryption

2 Data anonymization

3 Data integrity

4 Data access control

5 Data security challenges

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

更多Data Mining相关文章

更多相关阅读内容

How can you secure an expert system's data?

1

2

3

4

5

6

1 Data encryption

2 Data anonymization

3 Data integrity

4 Data access control

5 Data security challenges

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

查看其他技能