Data Risk Management 101

Security breaches and cyberattacks have become all too common in recent years. In the first half of 2022, an estimated 236.1 million ransomware attacks were reported worldwide. Meanwhile, between 2020 and 2021 the average number of attacks per company increased from 206 to 270. Perhaps most alarming of all is that the cost of cybercrime is projected to reach a staggering $10.5 trillion by 2025.

Stats like these make clear just how important it is for organizations to embrace sound data risk management practices to help keep their data safe. But what exactly is data risk management and why is it critical? Here, we’ll explore both, as well as some best practices to consider implementing to keep data secure.

What is Data Risk Management?

Data risk management refers to the processes, procedures, and controls that organizations can put in place to identify and minimize data risks. That includes any risks associated with how they acquire, store, process, transform, and use data across the entire business. Establishing and enforcing rigorous data risk management practices is essential to the success of any information security strategy because it is key to protecting not just the organization’s own data, but also that of its partners and customers.

For organizations that want to avoid data breaches, losses, manipulation, or unauthorized exposure, the importance of adopting a holistic approach to data risk management cannot be overstated.

What Are the Biggest Risks to Data?

To be clear, data itself isn’t inherently risky. It’s how organizations choose to handle their data that often exposes them to risk. The biggest threats to any organization’s data are often its own business practices. That typically includes:

  • Mismanaged data. Poor data access management occurs when organizations aren’t able to effectively acquire, validate, store, protect, or process data. Unsurprisingly, this can impede productivity while making it harder to facilitate effective collaboration and data-driven decision-making.
  • Lack of data governance standards. Data mismanagement is often the result of poor data governance, or more specifically, a lack of policies and procedures that dictate how their data will be handled. Effective data governance will clearly spell out who owns specific data, who can access it and when, whether it’s subject to relevant regulations, and what guardrails are in place to ensure its overall security.
  • Insufficient security practices. Whether it’s failing to regularly update the software being used across the organization to minimize potential vulnerabilities, not having a robust password management policy in place, or failing to require multi-factor authentication, not having the right data security practices in place – or not adequately enforcing them – can expose data to unnecessary risk.

Issues like these are often the result of other challenges that an organization might face, such as not having enough human or technology resources for the job. They’re often also a clear sign that the organization doesn’t have enough visibility into how its data is being used, perhaps because it’s relying on overly complex cloud infrastructure.

It’s also important to note that there are plenty of external factors at play that can put an organization’s data at risk. Phishing attacks, ransomware, and distributed-denial-of-service attacks are just a few examples of the ways in which bad actors regularly try to exploit their victims and steal their data.

Why Should You Prioritize Data Risk Management

In today’s world, data risk management isn’t a nice-to-have. It’s a business imperative that’s critically important for securing data operations, making those operations more efficient, maintaining data security, and ensuring that your organization always remains compliant with the latest compliance laws and regulations. More specifically, having a sound approach to data risk management can help organizations:

  • Obtain a holistic view of their data and understand how it’s being used
  • Identify and prioritize any threats to that data
  • Evaluate the potential impact of different risks
  • Develop strategies to mitigate those risks

Conversely, failing to adopt strong data risk management policies can lead to devastating consequences. These may include financial and reputational damage, a loss of customer trust, running afoul of regulatory requirements, business continuity disruptions, and reduced productivity.

Best Practices for Managing Data Risks

When crafting your data risk management strategy, keep the following best practices in mind to help ensure your success and minimize your risks:

Conduct regular data risk assessments. Reviewing your data, particularly your most sensitive data, can provide greater visibility into potential vulnerabilities so that you can proactively minimize them. A typical data risk assessment will include a data discovery and classification phase, an assessment of the risks associated with that data, and a risk mitigation phase where the organization takes specific steps, such as implementing safeguards or raising awareness among employees, to prevent potential issues.

Put access controls in place. Putting data access controls in place is a critical step toward reining in who is allowed to use your organization’s data, when, and for what purposes. Access controls rely on authentication and authorization to ensure that data users are who they say they are, and that they have the permission to access specific data. Attribute-based access control (ABAC) is particularly effective for data risk management because it dynamically enforces access control policies and requires little maintenance, so there is less risk of data or users slipping through the cracks.

Establish clear roles and responsibilities for personnel. From senior leadership to security officers and all data consumers, everyone within an organization plays a role in managing data risk. For executives, that includes leading by example and giving data security teams the resources and attention they need to be successful. Meanwhile, data architects and security officers must work together to set the actual policies and procedures that your organization will follow, while individual employees need to take responsibility for following those guidelines.

Implement data monitoring. Data monitoring is the process of proactively reviewing your data and assessing its quality. While this can encompass an array of factors, such as its completeness, consistency, and accuracy, it should also cover how secure your data is. Effective data monitoring will provide insights into how your data is being used and by whom, so that you have greater visibility into potential vulnerabilities.

Take advantage of anomaly detection. Anomaly detection is the process of analyzing your data, typically using artificial intelligence, to identify abnormal behaviors that could indicate a potential risk. By flagging data that’s out of the ordinary, anomaly detection can alert teams to potential issues such as fraudulent behavior so they can be addressed immediately.

While there are no silver bullets when it comes to data risk management, following the steps outlined above will go a long way toward keeping your organization’s data safe.

Mitigate Risk with Effective Data Risk Management

At a time of unprecedented threats to data, having a sound approach to data risk management is essential. It’s the best way to ensure you understand what data you have, how it’s being used, and where potential vulnerabilities lie so that you can proactively work to mitigate them. Find out more about building a holistic risk management framework.