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•  Firewall and Antivirus Software: Using firewall and antivirus software can stop and
                   alert  users  of  any  suspicious  activity  happening  on  their  devices.  With  the  timely
                   updated versions of the same, can go a long way in ensuring data security. Firewalls
                   use pre-configured rules to inspect all the packets entering and exiting a network and,
                   therefore,  help  stop  malware  and  other  unauthorised  traffic  from  connecting  to
                   devices on a network.

                                        •  Data Masking: It obscures data so that, even if criminals exfiltrate it, they can't make
                                          sense of what they stole. Unlike encryption, which uses encryption algorithms to
                                          encode data, data masking involves replacing legitimate data with similar but fake
                                          data. This data can also be used by the company in scenarios where using real data
                                          isn't required, such as for software testing or user training.

                    •  Training: Corporates must take up regular Data Security sessions of their staff to
                   sensitise them about following the data protection processes being implemented
                   and the importance of doing so. Making them conscious  of suspicious emails,
                   links that they might receive, not leaving their devices unlocked when unattended,
                   keeping software's up to date and not sharing passwords, are some of the things
                   that can be taken up.

                    •  Audits and Testing of Security System: Regular audits and testing of security policies, integrated malware
                   protection, firewalls, Wi-Fi connections security, Hardware-based security, checking applications security, email
                   security and compliance also play very important role in maintaining data privacy and providing data security.
                    •  Other Basic Preventions:  Being  aware  of  surroundings  and  threats  from  insiders,  complying  with  security
                   regulations which might be shared by entrusted agencies or bodies which track online cyber activities all across
                   the world are few other ways to provide cyber security.

                 Differences between Data Security and Data Privacy

                                     Data Privacy                                        Data Security
                 Data privacy ensures the ethical and lawful use of data.  Data  security  ensures  the  protection  of  data  from
                                                                       unauthorised access and breaches.
                 It focuses on how data is collected, used, shared, and   It focuses on safeguarding personal data, business
                 stored so that the rights of individuals over their data   data, intellectual property, and many more from
                 is protected.                                         various threats.

                 How are Data Security and Data Privacy related to AI?
                 Data security and data privacy are crucial components of Artificial Intelligence (AI).


                 Data Security in AI
                 AI systems often rely on vast amounts of data for training and operation. Unauthorised access and tampering
                 could lead to inaccurate AI models and compromised outcomes. Many AI applications process sensitive data,
                 such as personal, financial, or health-related information. Strong data security measures can stop data breaches
                 and unauthorised access.

                 Data Privacy in AI
                 Data  privacy  brings  the  ethical  use  of  AI.  This  ensures  that  AI  systems  comply  with  data  privacy  laws  and
                 regulations (such as GDPR, CCPA) to help protect individuals’ rights and maintain public trust. AI systems must
                 ensure that data is collected, shared, and used in ways that users have explicitly consented to, maintaining
                 transparency and trust.

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