Dear Jaswanth Kumar,

Below, based on my research, are the key techniques and types of security measures currently in use for cybersecurity of data stored in cloud computing. My research seeks answers to the following questions: how to secure a company's sensitive data from cyber criminals that is stored in cloud computing? How to secure Big Data in cybersecurity techniques using artificial intelligence technology? Below I have given the conclusions of my research.

Securing data stored in cloud computing is a key challenge for companies that need to protect their assets from cyber criminals. In response to the risks associated with cloud storage, a number of techniques and strategies have been developed to ensure the security of data, especially sensitive data and Big Data. Key techniques and protection methods using artificial intelligence are discussed below.

1. key techniques for securing data in cloud computing

(a) Data encryption (Encryption)

One of the primary techniques for securing data in the cloud is encryption, both at the transmission and storage levels. Encryption ensures that data becomes unreadable to unauthorized parties, and that decryption keys are available only to authorized users. It is important to use advanced encryption algorithms such as AES (Advanced Encryption Standard) and RSA to minimize the risk of the ciphertext being broken.

(b) Identity and Access Management (IAM).

Identity management and access control is crucial to restrict access to data to authorized users only. IAM systems allow strict management of roles, permissions and access policies, and implement multi-factor authentication (MFA), further strengthening security.

(c) Network segmentation and data isolation

Network segmentation involves dividing cloud infrastructure into smaller, isolated segments, making it more difficult for cybercriminals to move within the system once they gain access. Data isolation within different cloud layers further minimizes the risk of unauthorized access.

(d) Data Loss Prevention (DLP) security.

DLP technologies monitor the flow of data in the cloud and prevent data leakage. These systems detect suspicious behavior, such as sending large amounts of data to unauthorized locations or attempting to copy sensitive data.

(e) Application Security)

Application-level security used by cloud infrastructure includes regular updates, patching of software vulnerabilities, and implementation of technologies such as application firewalls (WAFs) that protect against SQL injection attacks, XSS (cross-site scripting) and other threats.

2. securing sensitive company data in the cloud

(a) The principle of minimizing access (Least Privilege).

This principle involves limiting access to sensitive data to only those employees who actually need it to perform their duties. This reduces the risk of accidental data leakage by unauthorized users.

(b) Auditing and monitoring (Continuous Monitoring).

Regular monitoring and auditing of access to sensitive data can detect any irregularities and anomalies in real time. Monitoring tools are able to identify suspicious activities, such as login attempts from unknown locations or unusual data operations.

(c) Encryption key management (Key Management)

Secure storage and management of encryption keys is a key element in protecting sensitive data. Many companies use HSM (Hardware Security Module) solutions to store keys in isolated, secure devices.

d) Secure Data Processing (Confidential Computing)

Confidential Computing is a technology that protects data not only during storage and transmission, but also during in-memory processing. This allows sensitive data to be operated securely without the risk of disclosure at the hardware level.

3. securing Big Data using artificial intelligence

Big Data is a huge amount of data that requires advanced methods of protection, especially in the context of cybersecurity threats. Artificial intelligence (AI) is playing an increasingly important role in securing Big Data due to its ability to analyze large data sets and detect threats more effectively than traditional methods.

(a) Machine Learning for Threat Detection.)

Machine learning algorithms analyze data in real time and are able to identify patterns associated with cyber attacks, such as phishing attempts, DDoS attacks and ransomware. AI-based systems learn from previous incidents, enabling them to detect new, previously unknown threats.

(b) Automated Incident Response.)

AI can automate response to attacks, which is particularly useful for large data sets where manual monitoring of threats would be time-consuming. Examples include automatically blocking suspicious IP addresses, restricting system access or isolating infected portions of the infrastructure.

(c) Anomaly Detection.)

Artificial intelligence technologies used for anomaly analysis are capable of detecting unusual patterns of user behavior and network traffic that may indicate an attempted attack or unauthorized access to Big Data.

(d) AI-Based Access Management of Access Policies.)

AI systems can manage access to Big Data dynamically, adjusting user privilege levels based on user behavior. If the system detects suspicious activity, it can automatically change access levels or block access.

(e) Artificial intelligence in data encryption (AI-Enhanced Encryption)

Artificial intelligence can support Big Data encryption by optimizing encryption mechanisms according to context and analyzing potential cryptographic security risks.

In view of the above, to effectively protect data stored in cloud computing and Big Data, it is crucial to implement advanced cybersecurity techniques such as encryption, network segmentation and access management, but also to leverage the potential of artificial intelligence. AI in cybersecurity allows for proactive data protection, faster detection of threats and automation of responses to attacks.

I conduct research in the problems of analyzing cyber-attacks carried out with ransomware viruses and in improving cyber-security systems. I have included the conclusions of my research in the following articles:

Analysis of the security of information systems protection in the con-text of the global cyberatomy ransomware conducted on June 2, 2017

Chapter Analysis of the security of information systems protection i...

Development of malware ransomware as a new dimension of cybercrime taking control of IT enterprise and banking systems

Chapter Development of malware ransomware as a new dimension of cybe...

Determinants of the development of cyber-attacks on IT systems of companies and individual clients in financial institutions

Chapter Determinants of the development of cyber-attacks on IT syste...

The Impact of the COVID-19 Pandemic on the Growing Importance of Cybersecurity of Data Transfer on the Internet

Article The Impact of the COVID-19 Pandemic on the Growing Importanc...

Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of Information with the Use of Sentiment Analysis and Big Data

Article Cybersecurity of Business Intelligence Analytics Based on th...

The role of Big Data and Data Science in the context of information security and cybersecurity

Article The role of Big Data and Data Science in the context of info...

THE QUESTION OF THE SECURITY OF FACILITATING, COLLECTING AND PROCESSING INFORMATION IN DATA BASES OF SOCIAL NETWORKING

Article THE QUESTION OF THE SECURITY OF FACILITATING, COLLECTING AND...

IMPORTANCE AND SECURITY OF INFORMATION PROVIDED BY THE INTERNET IN THE CONTEXT OF THE DEVELOPMENT OF ECONOMIC ENTITIES IN POLAND

Article IMPORTANCE AND SECURITY OF INFORMATION PROVIDED BY THE INTER...

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in an article of my co-authorship:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

Article APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTEL...

Increase in the Internetization of economic processes, economic, pandemic and climate crisis as well as cybersecurity as key challenges and philosophical paradigms for the development of the 21st century civilization

Article Increase in the Internetization of economic processes, econo...

CYBER SECURITY AND OTHER DETERMINANTS OF THE INTERNETIZATION OF LOCAL AND MUNICIPAL MAGAZINES

Article CYBER SECURITY AND OTHER DETERMINANTS OF THE INTERNETIZATION...

I invite you to join me in scientific cooperation,

Best regards,

Dariusz Prokopowicz

Both cybersecurity and cloud security are really important and offer good job opportunities. Cybersecurity is all about keeping our digital stuff safe-like the data on computers and networks. It's needed everywhere, so there are a lot of different jobs you can do, from being a security analyst to managing a team of cybersecurity experts.

Cloud security is a special part of cybersecurity that focuses on keeping data safe when it's stored online, or in the 'cloud.' As more companies are using the cloud to store their information, they need more experts to help keep it safe, especially because there have been some big security problems recently with big companies

Encompasses the protection of systems, networks, and data across all environments (on-premises, cloud, mobile, etc.).A subset of cybersecurity focused specifically on securing cloud-based infrastructures, platforms, and services. Cloud security protects cloud environments (like AWS, Azure, Google Cloud) from unauthorized access, data breaches, and cyber threats.

Article Cryptographic Algorithms: A Review of the Literature, Weakne...