Data breaches is a topic not new to database handlers. Some of the biggest risks today have not changed in the past 5+ years. In as much as many risks remain the same, they are still very real and pose an increasing threat to sensitive information loss and associated data breach costs. How organizations defend themselves and mitigate risk is as important today as ever before.

This article will guide you through sources and loop holes to these common dynamic data breaches

Dynamic Data Masking

In the recent past and contemporary society, databases have become increasingly vulnerable to attack. Two factors have contributed significantly to this escalation: First, organizations are being asked to grant increased access to data stored in the database. This additional access dramatically increases the potential for theft and abuse. Those that require access to data include internal employees, auditors, contractors, subcontractors, and supply chain partners. Secondly, database attackers have changed. In the past, people hacked into networks to “prove they could.” While those attacks were malicious, they seldom resulted in data theft.

This article highlights new and emerging security risks that IT professionals should factor into their defence strategy to curb Dynamic Data Masking breach;

Unpatched Vulnerabilities

Databases continue to experience increasingly sophisticated methods of data breaches. Attacks have advanced from recognizable exploits to more subtle methods that defy traditional intrusion detection mechanisms. Exploit scripts are posted to the web within hours of dynamic data patch releases. The availability of working exploit code, coupled with a 90+ day patch cycle (at most organizations) essentially leaves the keys to the database available for the taking.

Advanced Persistent Threats

Abbreviated as APT, these sources of data breaches are relentless, defined, and perpetrated by skilled, motivated, organized, and well-funded groups. No longer interested in the occasional bits and bytes, organized criminals and state-sponsored cyber-professionals are targeting databases where they can harvest data in bulk. These attacks target large repositories of personal and financial information. Once stolen, these data records can be sold on the information black market or used and manipulated by other governments.

Misconfigurations

Database misconfigurations provide weak access points for hackers to bypass authentication methods and gain access to sensitive information. Default settings may not have been properly re-set, unencrypted files may be accessible to non-privileged users, and unpatched flaws may lead to unauthorized data breaches.

Insider Data breaches

Common types of insider data breaches include password guessing or theft, privilege escalation, data theft, malware deployment, and denial of service attacks. Far too many people have access to data when there is no business need requiring these privileges. Or, employees may have excessive privileges that can be used to gain unauthorized access to sensitive dynamic data. Essentially, the more conduits there are on a corporate network, the more opportunities there are to exploit those access points – and the more that organization risks external attacks.

Insider Mistakes

Another potential source of data breaches on dynamic data masking is the “unintentional authorized user attack,” or insider mistake. The most common manifestations of this security incident type include the accidental deletion or exposure of data along with inadvertent, non-malicious security policy circumvention. The first risk occurs when an authorized user inadvertently accesses sensitive data and mistakenly modifies or deletes the information. The latter can occur accidentally when a user makes an unauthorized copy of sensitive information for the purpose of backup or “taking work home.” Although not a malicious act, it clearly violates organizational security policies and results in data residing on a storage device which, if compromised, could lead to an unintentional data breach.

Dynamic data masking

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