AI and Data Security: How to protect sensitive information and how to use AI properly

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Artificial intelligence (AI) has been making waves in almost every industry, and the industry of cyber security is no exception here. But using AI in cyber security has its pros and cons, as with any new technology. In this blog, we will take a closer look at the implications of AI in cybersecurity, data privacy best practices when applying AI, and what it means for your business.

Cybersecurity When Using AI

As the complexity and volume of cybersecurity attacks have grown in recent years, so have the technologies used to protect users. This growth is essential in the age of mobile devices (plus wearable devices), cloud computing, and wireless networks. Today, hackers have the means to attack different devices from any angle without detection.

One of the most critical advancements in cybersecurity is AI. Because of its capabilities to perform ‘smart’ tasks that a human does typically.

AI security is also better equipped to protect a company against the latest security threats today and in the future.

AI For Consent Management In The Case Of The GDPR

The different ways in which AI can be implemented for consent management in GDPR accordance are as follows:

  • Privacy Notice Generation: AI systems can be trained to generate privacy notices in simple language that is easily understandable by the data subjects. The privacy notice will explain the purpose of collecting the data, who it will be shared with, how it will be used, and how long it will be retained.
  • Consent Management: AI-powered consent management platforms will be used to get, store, and manage consent from data subjects. The platform can present privacy notices to the data subjects. It will allow the data subjects to give their consent securely. It also has the ability to maintain a record of the data subjects’ choices and record the date and time of consent.
  • Withdrawal of Consent: The AI-powered consent management platform will allow data subjects to withdraw their consent at any time. The withdrawal of consent will be recorded and respected by the data controller.
  • Data Access and Management: The AI system will assist with data management and access. For instance, securely sharing data with third-party service providers in accordance with the consent of the data subjects.

In this way, AI can help you to automate the consent management process. But the data controller is responsible for ensuring GDPR compliance. Therefore, it should thoroughly evaluate and test the AI system in order to ensure it meets the necessary requirements.

How AI In Cybersecurity Can Help Stop Cyber Attacks?

AI and ML are increasingly important in cybersecurity. They analyze large amounts of data to detect risks like malware and phishing. 

However, cybercriminals can even modify malware code to evade detection. ML is considered ideal for anti-malware protection as it can draw on data from previously detected malware to detect new variants. It works even when dangerous code is hidden inside innocent code. AI-powered network monitoring tools detect anomalies, track user behavior, and react accordingly. 

These technologies stop threats in real time without interfering with other business processes. They track the data that escapes human sight, such as emails, videos, chats, and other communications.

What Are The Applications Of AI In Cybersecurity?

  • Breach risk prediction
  • Phishing detection
  • Malware detection & prevention
  • User authentication
  • Spam filtering
  • Password protection
  • Bot identification
  • Behavioral analysis
  • Network segmentation & security
  • Fraud detection
  • Thread intelligence
  • Incident response
  • Vulnerability management
  • Identity & access management

Best Practices For AI And Data Privacy

Data Minimization

It refers to the principle that companies should only collect and process the minimum amount of personal data required to achieve their objectives.

Privacy by Design

It is an approach to developing AI systems by prioritizing privacy and data protection.

Anonymization and Pseudonymization

They are techniques used to protect personal data by replacing or removing identifiers that can link data to specific individuals.

Data Protection Impact Assessments (DPIAs)

DPIAs are assessments companies conduct to assess and identify the privacy risks associated with their AI systems. 

Conclusion

Artificial intelligence (AI) transforms how we work, live, and interact with the outside world. With the increased use of AI comes the responsibility to protect data and ensure that AI systems are deployed ethically and responsibly.

If you want to deploy such an ethical and responsible AI system that does not harm humankind, contact us today to discuss your requirements.

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