On average, enterprises spend $2.4 million and spend about 277 days trying to identify a data breach, plug it, and recover lost data. In today’s technological age, organizations acquire a huge amount more enterprise data than before. You may run into risks if you are not aware of who can access your sensitive data, which drives your business operations, and its location. An employee could be sharing confident information daily with others. It can be a list of passwords, internal documents, product roadmap, etc. Since employees use personal devices and cloud devices, there are high chances of sensitive data leaking because of a lack of data security.
Compliance exposure
Without a proper risk mitigation step in place, there is enormous compliance exposure faced in all industries. Privacy regulations cover the personal data of 75% of the world’s total population. Hence, it becomes crucial for security leaders and businesses to be aware of and implement an advanced data security lifecycle.
What is the data security lifecycle?
This framework outlines several steps that company executives need to take to safeguard sensitive data. It ranges from generation to identification to protection to disposal. It also includes major stages like data classification, data discovery, data protection, and data monitoring. The objective is to securely connect users with available resources. Data security controls data usage methods.
Data discovery
This involves identifying corporate data currently located in different corners of your enterprise. Data might be present on personal devices, in the cloud (SaaS, IaaS), or in a data center. The objective is to find out who has data access, view permissions for files, and ensure risk mitigation steps. Also, you can know who touched it and when it was last.
Users may have more access to sensitive data than is desired. Implement proper or minimum access for staff, allowing them to perform their duties. Data discovery is needed to comply with stringent regulations like PCI, HIPAA, and GDPR. Organizations need to adhere to privacy codes to safeguard personal data across industries like banking and healthcare. Non-compliance will only invite disaster.
Data protection
Data protection is the end objective to achieve. Consider the security implications as it helps control every interaction. Monitoring, classification, and discovery offer better insights to automate proactive enforcement and incident response. Identifying potential risks will prevent inappropriate data exchanges or downloads on all devices, whether unmanaged or managed. Automated data protection can help reduce data risk impact as well as prevent data loss before its occurrence. A breach can be contained, damage assessed, concerned stakeholders informed on time, and normal operations restored quickly.
Monitoring Data
Comprehensive monitoring is desired these days to evaluate risk and implement modern security measures. Data should be monitored constantly. Moreover, systems storing and processing the same should be monitored to identify suspicious activities. Real-time-based continuous monitoring enables the proper identification of potential threats. Thus, swift action can be taken to prevent sensitive data loss.
In addition, ongoing scanning detects abnormal activity in data, changes introduced, and interactions that could indicate risk. Audit reports and trials use vital information to match global regulatory needs. This intelligence diminishes the potential impact of exfiltration or a data breach. It is achieved by responding proactively to risks.
Data Classification
Data classification and discovery tend to go together. The former involves data type identification gathered by organizations. It also determines its sensitivity. PCI-type-regulated data, for instance, is more critical when compared to stale data. The latter is also stated to be trivial (ROT), obsolete, or redundant data. ROT data can be safely deleted. Machine learning and artificial intelligence help improve classification accuracy and speed, as well as eliminate human error that occurs in manual classification.
Therefore, understanding what exists can help enhance data security, thereby saving costs. It also ensures appropriate disposal for compliance and risk mitigation.
