Internet Research Project

Modern companies collect massive amounts of personal data including location, browsing history, and user behavior. While this data helps improve services, it creates serious privacy risks if improperly stored or accessed.

A major issue is data breaches, where hackers steal sensitive information from poorly protected databases. Companies like Facebook and Google have faced scrutiny for how user data is collected and shared with advertisers.

To protect privacy, organizations use encryption, firewalls, and strict access control systems. However, no system is completely safe, and users must also be careful with what they share online.

Example: The Facebook–Cambridge Analytica scandal showed how personal data was misused for political advertising.

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Computing resources such as servers, networks, and cloud systems can be misused for illegal activities like hacking, spamming, or launching cyberattacks.

One common misuse is DDoS attacks, where systems are overwhelmed with fake traffic, causing websites to crash. Organizations defend against this using traffic filtering and load balancing systems.

Cybersecurity teams also monitor systems 24/7 to detect suspicious activity and prevent unauthorized usage.

Example: The 2016 Dyn attack disrupted major websites like Twitter and Netflix.

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Unauthorized access happens when hackers gain entry into systems without permission, often by stealing passwords or exploiting software vulnerabilities.

Methods include phishing emails, malware, and brute-force attacks. These attacks can lead to identity theft or financial loss.

To prevent this, companies use multi-factor authentication, encryption, and regular security updates.

Example: The Equifax breach exposed personal data of over 147 million people.

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Computing innovations like artificial intelligence, cloud computing, and automation have transformed modern society. They improve efficiency, communication, and productivity.

However, these technologies also create risks such as job displacement, bias in AI systems, and privacy concerns.

For example, AI systems can make incorrect decisions if trained on biased data, affecting fairness in hiring or policing systems.

Example: Self-driving car accidents show both the promise and danger of automation technology.

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