What is Data Security Management?

Data security challenges

The most significant impact of a data security breach is losing business from current and potential customers.

The Ponemon Institute report points to poor passwords or credential management and misconfigured cloud data storage that allows public access as the top two causes of data security breaches. As organizations gather and store more information from digital networks, big data security challenges and cloud data security challenges increase. To avoid big data security threats and concerns, everyone in your business must be aware of big data security and privacy problems; such as:

• Security practices are deemed to conflict with agile development methods.
• Inadequate knowledge of how data flows into and through an organization.
• Organizations do not know why data has been gathered or created or how to maintain the data to be compliant.
• Misuse of email or other forms of social media or digital communication to exchange information (Dropbox or Zoom data sharing, for example).
• Lack of understanding of who has access to what and why and keeping that knowledge current as roles change or people leave.
• Allowing data to become forgotten (old or stale), representing an opportunity for a data breach or fines.
• Immature security and network protection (lack of proper encryption, firewalls, and vulnerability scans).
• Postponing updates to applications that include security patches.
• Lack of ability to monitor how staff working from home interact or share data.
• Allowing for the creation of unmanaged applications instead of improving applications to ensure ease of data use and exchange for employee tasks.
• Home networks or mobile device sharing or accessing corporate networks from public hotspots without the use of a secure VPN.

Data security best practices

The challenges are daunting, but below are recommended data security best practices that organizations can adopt and adapt to prevent data breaches, data loss, data leakage or avoid cybersecurity threats:

• A complete audit of data to map the lifecycle and lineage of data in your organization from acquisition to deletion. Once audited, unsuitable data must be cleansed or deleted to avoid security or compliance incidents.
• Data classification best practices are to maintain a catalog of data using master data management and metadata. Metadata, which acts like the cards in a library, helps applications or services know which data to use and how to secure it properly during or after usage. This underpins database security best practices.
• Restricting access to data according to its use and sensitivity.
• Accessing data only via approved APIs or applications.
• A zero-trust mentality should be used to assess all profiles that grant authorization to data by asking the question, does this role or service still require access and if so, why?
• All data maintained in physical devices residing in your data center or a cloud must have the same stringent security practices that apply to software and cloud services, including monitoring, alerting and reporting any access attempt, regardless of the reason.
• Data encryption best practices, while not foolproof, are one of the safest methods of ensuring data security, especially when combined with encrypting the data transfer.
• Cyber security threats are constantly occurring, and the methods of acquiring your data are evolving to overcome your security defenses. Performing continuous vulnerability scans and testing will help keep you safe.
• Database security best practices entail managing the schemas to meet the needs of the applications while also having a strong link to access management best practices and controls.
• Digital data will make use of cloud analytics for research and decision making. Power BI security best practices will scan and validate data prior to use to maintain integrity and classification of data.
• Training and enforcement of data security best practices with staff or using guides from security vendors can help show how easy it is to succumb to a hacker’s efforts.
• Data masking (hiding original data with modified content)) for the development of services is a DevSecOps best practice, especially for Personally Identifiable Information (PII).
• Benefit from external experts who test your networks, applications, or cloud services for data security concerns. Some firms have even hired hackers to perform these same activities.
• Have an incident management plan of what is precisely supposed to occur and how the breach is to be communicated internally and externally (especially to customers and regulatory agencies).
• Have a tested data recovery plan for those instances when data is inadvertently deleted or corrupted.
• Ensure applications or services can use any data backup, even ones created years before, to meet regulatory mandates, as software and hardware changes may negate their use. Accessibility of data in a secure fashion and reintroducing archived data is part of sound practices to prevent a data breach.
• Monitor to ensure that data is deleted when no longer required or becomes outdated.
• Train customers in data security management practices to build their trust that you consider data security to be important.
• Have a robust password management policy: minimum characters, how they can be derived, discourage previously used passwords, or benefit from a password management tool.
• Introduce multi-factor authentication, fingerprint or facial recognition to protect services and applications better.
• Validate your processes against data center security best practices and data management security best practice standards such as ISO/IEC27001 or NIST data security.
• Log all tests and keep them for audit and compliance teams.
• For mobile or digital devices:
  • Regularly update applications and security.
  • Install spyware or cookie blockers as appropriate.
  • Remove old applications.
  • Install mobile device blocking tools in case the device is lost or stolen.

Difference between data security and data privacy?

Data security helps to maintain the safety of data while your organization is using or storing it. Data privacy is the core practice of ensuring that personal or organizational information is maintained and used as stipulated by regulatory agencies or internal policy. Data security and privacy are similar in terms of data management overall concepts, but bear in mind that the main difference between data security and data privacy is that data privacy ensures information confidentiality, while data security keeps your data and organization safe.

Personal or organizational information is a valuable and tradable commodity, so your data privacy solutions must be able to:

• Prove that you obtained the information legally and with the consent of the other party.
• Show how your practices blend cyber security and privacy practices such as those from NIST.
• Explain how roles for data access are derived, monitored and alerted when a breach occurs.
• Illustrate how big data security and privacy (manual data, cyber data, application data) is maintained and secured against loss, leakage and incidents.
• Fulfil requests as required by governmental regulations for the deletion of privacy data if requested by the owner (GDPR).
• Alert and respond to all those impacted by a data incident promptly.
• Backup and restore information to mitigate threats to data.
• That data will be backed up or stored safely and only for as long as needed.

Regular monitoring of compliance, updating privacy policies as governmental or best practices change, and ensuring that data security software, training and processes underpin data privacy is an organizational commitment owned by senior leadership.