Four Data Security Challenges Set to Impact Organizations in the Coming Years

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Data is the most durable, limitless commodity driving future economies and offering fresh insights for prosperity and betterment. Yet, a data-rich world introduces multiple security risks for organizations, industries, and society. Here are four security challenges likely to impact organizations in the coming years.

1. Rapid Tightening of Regulations Raises the Stakes for Non-Compliance

As fears and pressures relating to data privacy mount, governments worldwide will introduce much tougher sanctions regulating the use of data. This rapid tightening of national and international trade rules catches many organizations off-guard, leading to hefty fines and legal fees. Organizations that do not establish clear roles, responsibilities and processes for continuous monitoring and assessment of the regulatory landscape will often find themselves exposed to increased security risk with the possibility of substantial regulatory backlash. 

2. ‘Shadow Data’ Becomes the New Smoking Gun

While organizations continue to grow their data sprawl and embrace so-called datafication, a new security risk relating to data hidden outside corporate IT governance emerges: shadow data. Negligent organizations unaware of the risks and legal ramifications struggle to distinguish worthy data from noisy data. They continue to pile on shadow data, silo after silo, and eventually this bubble explodes, leading to compliance failures, regulatory reprimands and reputational damage. 

3. Low-cost Storage Encourages Data Hoarding but Comes with a Cost

Humans tend to be natural hoarders. As the cost of storage decreases, organizations will be encouraged to hoard more data rather than rationalizing it. (In simple terms, data rationalizing is the process of mapping out data to identify and analyze copies and overlaps.) This gives rise to hidden security risks and operational costs that may not be apparent at first but can compound gradually over time as the volume of data being hoarded increases. Moreover, if hoarded data is poorly monitored, eventually it becomes an attractive target for cybercrime.

4. Poor Data Quality Leads to Poor Outcomes

As organizations seek to quickly monetize the vast amounts of data available, they sometimes cut corners, overlooking data authenticity and integrity checks, compromising data quality in exchange for quick profit. Sometimes, organizations will blindly assume that their data are good quality. When dirty data and misguided assumptions are fed as inputs for training machine learning and AI models, they exponentially amplify the data quality, biases and integrity, resulting in poor business decisions and negative business performance. 

How Can Organizations Prepare for These Risks?

Almost all aspects of our everyday lives are increasingly data-driven, from the workplace to innovation to commerce. This is why organizations must recognize the security risks implicit with data and seek out strategies for implementing mitigations that can counter these risks. Below are a mix of best practices that can help:

  • Compare current legal and regulatory requirements versus current data processing activities. Implement a plan of action to close identified gaps.
  • Monitor for changes in legal and regulatory provisions and assess the impact in the business context. 
  • Perform a thorough evaluation of processes that ingest unstructured data from a data quality and integrity standpoint.
  • Identify business units that are contravening a policy, are operating informal activities, or are generating shadow data. Try to formalize those activities or cease them if inappropriate.
  • Evaluate the data lifecycle and current state of data storage utilization and identify opportunities for rationalization. Explain to the business the hidden costs and security challenges surrounding data hoarding.
  • Educate employees on the importance of data privacy, data quality and the need for effective protection and governance processes. 
  • Engage regularly with business stakeholders, further promoting the importance of data sanctity, the purpose of security functions and the need for a shift-left culture (giving developers a voice in security governance).
  • Evaluate supply chain activities involved in data processing and ensure these are operating within the regulatory framework. 

Regardless of how little regulatory oversight may seem to be or how questionable the value of data may appear, organizations need to develop a better understanding of their data and implement controls that help protect and manage its confidentiality, integrity and availability. 

Even the most modern organizations will struggle to succeed if they experience the most basic security issues that impact availability. In other words, even if an organization does not believe that its data is particularly sensitive or valuable, it is still important to take steps to protect it. This is because data breaches can have a significant impact on an organization’s reputation, finances and ability to operate.

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