How data risk management can protect a company
Data risk management is the process of identifying, assessing, and mitigating risks related to: data security, data privacy, data quality, and data governance.
Data is a real asset, a value creator, and a business enabler. Practices to protect and valorize them are a pillar of a company’s risk management strategy.
In a study from McKinsey, it is shown that resilience of global value chains is highly impacted by the way risk is managed in data value chains.
Mitigating risks in distributed value chains
In a distributed environment, with complex supply chains operating in many countries, risk management must consider two main areas: value chain risk management and data transfer risk assessment. These are crucial tools for preventing and reducing the impact of negative events.
Value chain risk management
The whole ecosystem that generates value for the organization must be modeled completely and transparently. Consequently, value chain risk management analyzes cross-functional and cross-company processes that may have an impact on business operations continuity and stakeholders. Often a risk management dashboard presents risks transparently along the value chain, with an interface to suppliers to inform them. In that way, the organization and the whole value chain have the skills and KPIs (Key Performance Indicators) to ensure process security and the right mindset to identify the best possible solutions and remediations to vulnerabilities.
Cybersecurity is a major driver of risk mitigation in supply chains:
ENISA - Supply chain cybersecurity
Data transfer risk assessment
Data transfer risk assessment is an issue for almost all businesses because they must operate across multiple borders and jurisdictions in today’s global world. To do this effectively, companies need the ability to share relevant data with parties who need it. The main challenge is how to share data efficiently while following the growing number of international data privacy laws. This is especially difficult due to the increasing need for data localisation in many countries, such as China and the Middle East. To address this, organizations must first determine what data needs to be shared with whom and whether there is a requirement to include personal data in datasets that flow between jurisdictions. The transfer risk assessment (TRA) is a key process not only for personal data but is becoming a must also for industrial and commercial data in the EU, with the upcoming Data Act and Data Governance Act.
EY released a study on international data flow challenges:
EY - Data transfer challenges
Business Impact Analysis
In a consistent strategy, the Business Impact Analysis (BIA) is a key step in the continuity planning process. The BIA enables the Business Continuity responsible team to fully characterize the systems requirements, processes and interdependencies in order to use this information to determine continuity requirements and priorities. The BIA aims to link specific IT components in the infrastructure with the critical processes they support. Understanding this information helps identify the effects of disruption on these vital resources. Results from the BIA should be appropriately incorporated into the analysis and strategy development efforts for the IT Disaster Recovery Plan, Business Recovery Plans and the Incident Management Plan.
Types of data risks across companies
The growing complexity and number of interconnections in industrial ecosystems drive the challenge to a higher level. This is why, without a serious data risk management plan, companies may face different types of data risks during their operations:
- Confidentiality risks: these refer to the unauthorized access or disclosure of sensitive or confidential data. This could include data breaches, insider threats, or accidental disclosures.
- Integrity risks: unauthorized modification of data, which could lead to errors or inconsistencies in the company's records. This could include cyberattacks, rogue employees, or equipment failures.
- Availability risks: the disruption of access to data, which could prevent the company from being able to use it effectively. This may happen as a consequence of cyberattacks, natural disasters, or hardware failures.
- Compliance risks: not complying with relevant laws and regulations related to data protection, such as the General Data Protection Regulation (GDPR) in the European Union and the upcoming regulations on industrial data, such as the Data Act and Data Governance Act.
- Reputational risks: potential damage to the company's reputation that could result from a data breach or other data-related incident.
- Legal risks: potential legal liabilities that the company could face as a result of a data breach or other data-related incident.
- Financial risks: potential financial losses that the company could incur as a result of a data breach or other data-related incident, such as the costs of responding to the incident or compensation payments to affected parties.
Often, the perception of risk makes business owners scared when they have to start digital projects or promote innovations with their stakeholders. Businesses have access to a large amount of data, but much of it goes unused. Many projects fail due to fears and inadequate risk management when it comes to managing data across value chains. The awareness and monitoring of the risk level is a fundamental benefit of a data risk management strategy in action because it enables the creation of new value through effective collaboration and trust between the parties involved in the business.
Main processes in data risk management
Planning and practicing data risk management means relying on consistent processes that must be deployed in the whole data value chain:
- Risk assessment: it is the process of identifying and evaluating the potential risks to the company's data. This may involve analyzing the company's data assets, identifying potential vulnerabilities, and assessing the likelihood and impact of potential data-related incidents.
- Risk control: implementing measures to mitigate or eliminate identified risks. This may involve implementing technical controls, such as encryption and access controls, or developing and implementing data protection policies and procedures.
- Risk monitoring: regularly reviewing and monitoring the effectiveness of the company's data risk management measures. This may involve conducting regular risk assessments and reviewing the company's data protection policies and procedures.
- Risk response: having a plan in place to respond to data-related incidents and minimize their impact. This may involve establishing incident response protocols and conducting regular drills to test the effectiveness of the response plan.
- Risk communication: ensuring that all relevant parties, including employees, suppliers and customers, are aware of the company's data protection policies and procedures and how to report any potential data-related incidents.
Overall, an effective data risk management strategy should involve a well-mixed combination of these processes and should be regularly reviewed and updated to ensure that it remains proactive. Risks related to data assets are always evolving when new technologies and business innovations are deployed in the market.
Procedures for evaluating risk in data processing are outlined by ENISA:
ENISA - Risk level methodology
Data risk management strategies: how to implement it efficiently?
In our experience, very few organizations can count themselves as leaders in data risk management, nor use it to establish a competitive/business advantage. On the other hand, companies that consider it a key strategic differentiator take a big competitive advantage.
How can a savvy management team preserve their organization and extended value chain from events that may cause disruption or high-impact damages and losses to their business operations? We can make a list of some best practices. Fully complying with all the rules simultaneously is exceedingly difficult, so organizations need to take an agile approach as the landscape shifts.
To build and deploy an effective strategy and efficient processes for risk mitigation, we can identify several key steps that a company can take to protect itself through data risk management at the value chain level:
- Identify and classify the data: it is important to understand what data the company has or shares in its value chain, where it is stored, and how it is used. This will allow the company to prioritize the data and identify any potential risks.
- Develop and deploy a data protection policy: a comprehensive data protection policy can help the company ensure that it is complying with relevant laws and regulations, as well as setting out clear guidelines for employees to follow when handling data.
- Implement technical controls: technical controls, such as encryption, secure data sharing technologies and access controls, can help protect data from unauthorized access or tampering.
- Conduct regular risk assessments and audits: regular risk assessments with KPIs derived from the whole data value chain can help the company identify and address potential vulnerabilities in its data protection systems.
- Educate and train employees: it is important to ensure that all employees are aware of the company's data protection policies and procedures and that they know how to handle data responsibly.
- Review and update policies and procedures regularly: as the company's data and business needs evolve, it is important to regularly review and update data protection policies and procedures to ensure they remain effective. Data policy management is no longer just focused on maintaining regulatory compliance. Instead, organizations are beginning to recognize the business-driving benefits of achieving effective data policy management frameworks.
- Respond and recover: it is necessary to establish an incident response plan to address security breaches or other incidents. Have a well-defined process for recovery and business continuity.
To improve your data risk management strategy and get more value from your data assets, consider looking into secure data-sharing platforms like GUARDA. It solves many of the issues we have addressed in this article.
With 30 years of management experience, a solid engineering background and lifelong passion for innovation, I currently serve as COO of Security Forge, the cybersecurity startup that develops technologies and products for secure data sharing in industrial value chains. My mission is to bring competencies and multisectorial expertise gained from both large ICT corporates and SME organizations where I worked during my professional career, also as a mentor, advisor and consultant.