6 Best Practices for Data Risk Management

In 2010, a BP-operated offshore drilling rig in the Gulf of Mexico experienced a massive explosion and subsequent oil spill. Before the incident, known as Deepwater Horizon, BP failed to implement adequate risk management measures and heed warning signs, contributing to the disaster. The extent of the catastrophe reveals what can happen when risks are not properly addressed and warning signs are ignored. 

Data-driven businesses, under pressure to maximize data usage, drive growth, and remain competitive, frequently prioritize their pursuit of data-driven strategies and innovation. Often, they, too, disregard warnings about risk management. In doing so, they may overlook or downplay potential risks, compromising the security and integrity of their data assets. The potential consequences may be equally severe, though not as visibly dramatic. 

However, businesses can mitigate these risks by implementing proactive and comprehensive data risk management strategies, particularly around data sharing. Such strategies play a crucial role in safeguarding data integrity and confidentiality, ensuring protection against unauthorized access, and corruption during transmission or storage.

Maintaining regulatory compliance often depends on data risk management and mitigation. However, these practices serve a purpose beyond compliance and proactively reduce the risks associated with data sharing, including data breaches. These practices establish a robust foundation for data protection and elevate overall cybersecurity posture.

To further understand the complexities of data risk management, let’s explore the various types of data risks and their underlying causes.

Data risk management in enterprises

Like the intricate, interconnected systems in offshore drilling operations, data analytics and risk management requires a clear understanding of potential risks inherent to data movement. With this understanding, teams can implement robust, proactive safety measures that prevent catastrophic incidents for the organization and its stakeholders.

The data risks that enterprises may incur with data movement include:

• Data loss or corruption
• Security breaches and loss of data privacy
• Data inconsistency and quality issues
• Operational disruptions
• Regulatory and compliance risk

Data risk management is intricately linked to data sharing, as it involves sharing sensitive information. In data and privacy breaches, unauthorized individuals access sensitive data and potentially compromise enterprise security and shared data integrity. Other data risks, particularly data manipulation, can distort insights, enable fraudulent activities, and compromise decision-making.

Security vulnerabilities, including programming errors, design flaws, or improper configurations, also present a significant threat to data. Malicious actors can exploit these vulnerabilities to gain unauthorized access to valuable information, compromising its confidentiality and integrity. 

Data misuse like unauthorized data use, sharing data without proper consent, or data usage policy violations is also problematic. When employees misuse data, they risk privacy violations, legal consequences, and erosion of trust in data-sharing practices, which all have far-reaching negative impacts.  

Enterprises must ensure data integrity, accuracy, consistency, and reliability to maintain user trust in data-driven processes. Failure to dod so can compromise the effectiveness of shared data. Data re-identification and de-anonymization pose significant risks compromise personal information by linking anonymous data to specific individuals or removing anonymity from previously anonymous data. Data loss from inadequate data access controls can also lead to unauthorized access or leakage.  

Consider a scenario where a financial institution experiences data loss due to inadequate data access controls. In this case, confidential customer information, including names, addresses, financial transactions, and account details, is inadvertently exposed or leaked. Without proper data access controls, unauthorized individuals can access sensitive financial data, putting customers at risk of identity theft, fraud, and economic harm.

Build your data risk management framework

A data risk management framework must clearly establish objectives that lay the foundation for a roadmap that identifies potential risks, defines risk tolerance levels, and implements safeguards for data assets.

Identify potential data risks

To identify and assess potential data risks, you must: 

• Conduct comprehensive risk assessments 
• Analyze the data landscape 
• Evaluate existing security measures 
• Consider potential vulnerabilities and threats that could compromise data integrity and confidentiality

Document the identified risks, assess their impact, then assign specific individuals or teams to implement mitigation measures and monitor risk levels.

Define risk tolerance levels

Your organization must also determine the level of risk it is willing to accept and tolerate. Consider factors such as regulatory requirements, business objectives, and the sensitivity of the data involved. A manufacturing company with a high risk tolerance may adopt innovative automation technologies, even if it introduces potential operational disruptions, to achieve greater efficiency and gain a competitive advantage

Implementing best practices for data risk mitigation

Best practices for data risk mitigation involve a range of strategies and measures that enterprises can adopt to protect their valuable data assets. These practices include:

• Classify and encrypt your data
• Establish access controls and user authentication
• Enact consent management
• Build a data breach response plan
• Create data sharing agreements and contracts
• Implement network security measures

Classify and encrypt your data

Data classification systematically categorizes and labels data based on sensitivity, then applies appropriate security measures. Classification also specifies data that requires encryption, then apply the appropriate level of protection. 

Revelate integrates with Immuta to offer robust capabilities for classifying and categorizing sensitive data based on predefined criteria. The platform also uses encryption to protect data at rest and in transit, ensuring that confidential information remains secure and inaccessible to unauthorized individuals. Together, these features enable enterprises to establish a strong data protection framework that safeguards sensitive data from unauthorized access and potential breaches.

Establish access controls and user authentication

Access control is a critical aspect of data risk management. Role-based access controls (RBAC) assign permissions and access rights to users based on their specific roles and responsibilities. This ensures that individuals only have access to the data and systems necessary for performing their job functions. 

Strong user authentication mechanisms require users to verify their identities through multi-factor authentication before gaining access to sensitive information and systems. 

Revelate, with its advanced data governance capabilities, automates the implementation and enforcement of RBAC and user authentication, streamlining the process and ensuring consistent adherence to security protocols across the organization.

Enact consent management

Obtaining proper permissions and approvals is essential for conducting activities that might exacerbate privacy concerns or violate data protection regulations. Consent management, which is the systematic process of obtaining explicit and informed consent from individuals to collect, use, and disclose their personal data, is a key part of maintaining ethical, compliant data practices. 

Revelate enables organizations to effectively capture, store, and manage individual consent preferences and permissions for data collection, use, and sharing. The platform’s robust functionality ensures compliance with privacy regulations and fosters transparent and accountable data governance practices.

Build a data breach response plan

A data breach response plan helps organizations mitigate the impact of a security incident, much like the emergency response plans implemented in the aftermath of the Deepwater Horizon incident. A well-defined response plan empowers businesses to promptly address data breaches, minimize data exposure, and safeguard affected individuals.

An effective data breach response plan establishes clear roles and responsibilities, fostering accountability, efficient decision-making, and coordinated actions among team members. Establishing communication protocols enables a swift and effective data breach response with clear lines of communication, timely information sharing, and coordinated stakeholder actions.  

Revelate integrates with Immuta to effectively detect, mitigate, and respond to data breaches. Advanced features for incident management, breach notification, and remediation, allow businesses to establish proactive measures and protocols to minimize the impact of data breaches.

Create data sharing agreements and contracts

Data sharing agreements and contracts ensure proper data handling, security measures, and regulatory compliance. These agreements define the terms and conditions for data sharing, including the responsibilities and obligations of the parties involved, data ownership rights, and the necessary security measures to protect shared data. Addressing these issues effectively establishes a framework for maintaining data integrity, confidentiality, and compliance within data sharing agreements. Once implemented, they promote transparency, trust, and accountability in data sharing practices.

Revelate enables businesses to establish secure and compliant data sharing practices. This in turn ensures the proper handling, sharing, and protection of sensitive information in accordance with the defined terms and conditions of the agreements. Additionally, Revelate automatically tracks and logs data-sharing activities, providing detailed insights into who accessed which data set, when, and from where, making it one of the most secure cloud services available.

Implement network security measures

Firewalls and intrusion detection/prevention systems are protective barriers or containment measures in the digital realm, much like the containment strategies employed in response to the oil spill. Both prevent unauthorized access or the spread of harmful elements, be it data breaches or environmental damage.

Enterprises rely on these security measures to monitor and control network traffic, detecting suspicious activities in real-time, and preventing unauthorized access. They play a crucial role in minimizing the risk of data breaches and ensuring the integrity and security of sensitive information.

Relevate offers centralized control and visibility over security measures, allowing businesses to configure and customize firewall rules, set up intrusion detection systems, and receive real-time alerts for potential security breaches. The platform integrates with the existing security infrastructure, enabling businesses to maintain a robust defense against unauthorized access.

Assess regulatory compliance and legal consideration

Relevant data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), impose legal obligations on organizations to protect individual personal data. These regulations outline data collection, processing, storage, and sharing requirements and provide individuals with rights regarding their personal information.

Compliance with data privacy laws and regulations requires companies implement certain data handling policies and procedures. Enterprises should conduct regular audits and assessments and provide ongoing staff training to ensure ongoing data privacy compliance. 

Had BP demonstrated a stronger commitment to stringent safety protocols, they may have mitigated the impact of the Deepwater Horizon oil spill. Similarly, businesses should prioritize proactive measures to prevent data breaches now, so that potential future compliance violations or security weaknesses are as contained as possible.

Evaluate and improve data risk management strategies

Revelate’s data monetization maturity model evaluates an enterprise’s data sophistication (ability to operationalize data) and capability (ability to share data). We consider a company to have “high data maturity” if they:

• Understand data governance, compliance, and licensing
• Consistently monitor and evaluate how effective their data risk management practices are
• Strive for continuous improvement and adherence to industry standards
• Conduct regular assessments of risk controls
• Conduct risk assessments to identify potential risks
• Analyze data breach incidents to enhance their data security measures

Data risk management: the road to high data maturity

The Deepwater Horizon incident highlighted BP’s failure to prioritize safety and environmental responsibility, leading to a loss of confidence among customers who expected the company to operate with integrity and protect the environment. The oil spill and the subsequent mishandling of the situation by BP damaged the company’s reputation and eroded the trust that customers had in the brand. 

The incident should serve as a reminder for enterprises to prioritize the safeguarding of what customers value most within their realm of responsibility. Enterprises that prioritize the protection of sensitive information not only earn the trust of their customers and stakeholders, but also enhance their brand reputation and foster stronger customer relationships. Thus, data risk management can become a critical aspect in gaining a competitive edge in the market, as increased customer satisfaction and positive word-of-mouth recommendations lead to sustainable growth and long-term success.

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