This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Data Privacy

Data Risk Mitigation: How To Keep Your Organization’s Data Safe

Sam Noss, July 28, 2023

Understanding Security Risks

Digital technology’s proliferation means the biggest businesses collect and store massive amounts of sensitive data, including personal information about customers and employees. 

Companies are responsible for protecting this sensitive information from unauthorized access and other data security risks. This blog focuses on data risk mitigation issues, concepts, and strategies to help organizations recognize their vulnerabilities and proactively implement risk management functionalities.

Data Breaches and Cyberattacks

Currently, cyberattacks, data breaches, and phishing scams impact more people than ever and are costlier to deal with. IBM’s Cost of a Data Breach Report 2022 lists the average cost of a single data breach in the United States and around the globe at $9.44 million and $4.35 million respectively.

Companies failing to adequately protect personal, sensitive information from vulnerabilities can expect financial health issues, and run the risk of suffering long-term reputational damage and a loss of customer trust.

As such, strong data security and cybersecurity programs are crucial aspects of risk mitigation.

Common types of cyberattacks include but are not limited to:

  • Ransomware and Malware
    • All ransomware is malware, but not all malware is ransomware. Malware is software designed specifically to infiltrate and disrupt, damage, or gain unauthorized access to a system. While ransomware falls under the malware umbrella, it’s used to deny victims access to their files or systems and demand a ransom in exchange for returning access.
  • Phishing and Social Engineering
    • Similar to the relationship between ransomware and malware, phishing is a type of social engineering attack. Social engineering attackers use social skills to psychologically manipulate victims to share sensitive information or take action that helps an attacker. Where social engineering can take place in any setting, in-person or over technological platforms, phishing is limited to giving out information, passwords, or money via some type of technology.

Identifying Vulnerabilities

One of the first steps in the risk mitigation process is identifying and assessing the organization’s data vulnerabilities and potential risks. Part of this process involves accounting for the amount and type of sensitive data and personally identifiable information (PII) held by an organization so there’s a clear understanding of what data needs to be protected and how.

Generally, there are 4 types of data classifications. These are listed below by their level of sensitivity:

  1. Public Data (Least Sensitive) — Any information that can be made freely accessible by anyone or is already (e.g., public records, press releases, and promotional materials). This data type may be considered public, but the data owner is still subject to some situational regulations when it comes to sharing, storing, and organizing data.
  2. Internal-Only or Internal Data: Any information restricted to an organization’s employees or members (e.g., business plans, internal communications). This data type can’t be shared outside of the organization.
  3. Confidential Data: Any sensitive information that requires elevated access permissions even within the organization, but won’t result in legal consequences if confidentiality is violated. This data is inaccessible without specific, role-based rights and isn’t shareable unless the recipients have been granted those same access rights.
  4. Restricted Data (Most Sensitive): Any information carrying significant legal and regulatory penalties should access violations occur, likely resulting in criminal charges and substantial fines. This data is generally protected under a compliance framework or would severely damage the organization if released (e.g., sensitive customer or employee personal data, proprietary research and development, etc).

Classifying data according to the above categories allows those responsible for data risk management to quickly determine if sensitivity-based protections are present and appropriately enforced.

Risk Management and Mitigation Strategies

Risk Assessment and Decision-Making

Conducting ongoing cybersecurity risk assessments and Data Protection Impact Assessments/Privacy Impact Assessments (DPIAs/PIAs) to identify potential risks and understand an organization’s risk level is essential for mitigating data security issues. However, while essential, these assessments can include massive amounts of data and be deeply complex.

Manual data risk assessment process:

  1. Inventory sensitive data
  2. Assign data classifications
  3. Cross-functional assessment team decides which sensitive data to prioritize
  4. Review related security and privacy controls
  5. Fulfill assessment and document privacy/security shortcomings and potential risks

General DPIA/PIA process:

  1. Assess the personal information — mundane vs sensitive — being collected, its collection purpose, and the ethical implications
  2. Determine the nature and benefits of the processing activity
  3. Consider how data may change or switch hands, including the possibility of it being shared outside the organization
  4. Identify if the team can ensure proper usage through organizational, contractual, and technical controls, including appropriate security safeguards
  5. Take steps to mitigate discovered risks and correct operational deficiencies
  6. Set a time or guideline on when to revisit the assessment — for example, if there’s a material change in the activity
  7. Periodically reassess the effectiveness of the organization’s DPIA/PIA process

Implementing these processes provides clarity about a company’s risk level, leads to better decision-making when it comes to mitigating data risk, and lays the foundation for a comprehensive risk management plan.

Data Governance and Regulatory Compliance

Data governance is the overall set of actions to ensure stakeholder and customer data is secure, private, accurate, available, and usable. Better governance also helps to ensure compliance with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). 

The steps to implement a data governance framework:

  1. Define data strategy, goals, and objectives
  2. Secure essential stakeholder and executive support 
  3. Assess, build, and refine the data governance program
  4. Document organizational data policies
  5. Establish governance roles and responsibilities
  6. Develop and refine data processes
  7. Implement, evaluate, and adapt strategy

Security Measures and Best Practices

Implementing data security policies and procedures for a company-wide data risk mitigation program is essential, but not always easy or straightforward. Ensuring data privacy and protecting sensitive data to reduce business risk, maintain stakeholder and customer trust, avoid illegal practices, and prevent incurring massive costs means building a strong data management program with the non-exhaustive list of tools below.

Risk Management analysis

Risk management analysis is an ongoing process that helps implement a Data Security Management strategy by continuously monitoring for new and novel threats. The analysis is made up of four cyclical steps: 

  1. Identify existing risks
  2. Assess the risks, evaluate threats
  3. Handle the risks, implement measurements
  4. Control the risk, continue risk monitoring and reporting

When you finish step 4, cycle back to step one and continue monitoring for new risks.

Least Privilege and Access Control principles

Implement the principle of least privilege to limit accessible data, resources, applications, and application functions only to those a user requires to execute their daily tasks. Without least privilege controls, organizations create over-privileged users or entities that increase the potential for breaches and misuse of critical systems and data.

Access control allows companies to manage authorizations for organizational data and resource access. A strong access control system should be able to accurately verify and provide correct access roles to various users. There are many access control systems to choose from, like Attribute-based Access Control (ABAC), Discretionary Access Control (DAC), Mandatory Access Control (MAC), and more. 

Strong Passwords and Multi-Factor Authentication

Cybercriminals are getting better at decrypting passwords, and your organization needs to prepare by implementing strong passwords from the start. Paring company-wide password guidance with multi-factor authentication is an easy way to make your data security program stronger across the organization. 

Consider single sign-on (SSO) authenticators, multi-factor authentication apps, and password managers when building your cloud security strategy.

Data Privacy Training for Employees

Data privacy and data privacy awareness training refers to educational courses and programs designed to educate employees about data privacy regulations, best practices, company privacy procedures, and the importance of protecting sensitive information

These training courses typically cover various topics like compliance training, security awareness training, the importance of training programs, privacy-specific training content, and training requirements. Organizations can mitigate the risks associated with data breaches and privacy violations by equipping employees with the knowledge and skills needed to handle data securely.

The Human Factors in Data Risk Mitigation

Employee Education and Phishing Awareness

As mentioned, employee privacy education on data security best practices and more can help further secure an organization’s data risk mitigation program. Training content should focus on practical examples, case studies, and interactive exercises to engage employees and reinforce their understanding of data privacy principles and best practices.

Some training ideas:

  • Introduction to Data Privacy: Provide a foundational understanding of data privacy, its importance, and the potential consequences of privacy breaches.
  • Data Protection Regulations: Educate employees on relevant data protection regulations like GDPR, CCPA, HIPAA, and any other applicable laws, including the rights of individuals and the organization’s obligations.
  • Identifying and Handling Sensitive Data: Teach employees to identify different types of sensitive data (e.g., PII, financial data, etc.) and explain the proper handling and storage procedures to ensure confidentiality and integrity.
  • Security Best Practices: Cover essential security practices, including strong password management, multi-factor authentication, secure file sharing, avoiding suspicious links or attachments, and maintaining up-to-date software and antivirus protection. This is a great place to introduce company-specific data security and privacy norms.
  • Social Engineering Awareness: Raise awareness about social engineering tactics like phishing emails, impersonation attempts, or pretexting, and provide practical tips to help employees recognize and respond to these threats appropriately.

Minimizing Human Error and Insider Threats

Human error is inevitable — we make mistakes. However, reducing the chance for human error and insider threats via automation and machine learning is a great way to outsmart and mitigate data risk. Cultivating a security-first environment throughout an organization is a great step toward protecting data throughout its lifecycle, securing company endpoints (devices), and preventing data loss.

Laying the groundwork of a data management program with access and authentication controls like MFA and ongoing, highly-automated PIAs/DPIAs signification reduces the chances of insider threats — intentional or not — damaging a company.

Data Risk Mitigation Today

Emerging Threats and Security Risks

Mitigating risk isn’t a one-and-done process. It’s important to continue researching data privacy and security news and trends to make sure organizational data risk management processes are up-to-date and ready for whatever comes next. 

For example, the rise of artificial intelligence (AI) is already revolutionizing data risk management by improving data security, preventing cyber threats, and reducing the risk of data breaches. However, AI is also being leveraged by bad actors and hackers to improve cyberattack strategies. 

An organization keeping its finger on the pulse of emerging technologies is well-placed to understand and mitigate forthcoming potential risks.

How DataGrail Helps Mitigate Your Data Risk

DataGrail is a data privacy platform, but privacy and security go hand-in-hand. Our Privacy Control Center helps mitigate data risks by ensuring regulatory compliance with all privacy laws, automating privacy workflows and controls like data subject request (DSR) fulfillment and PIA/DPIA completion, and providing privacy insights with our comprehensive dashboard.

DataGrai seamlessly integrates with existing systems and data infrastructure and makes it easy to connect new SaaS applications and external systems thanks to our industry-leading Integration Network boasting 2,000+ connectors.

Provide customers and stakeholders with transparency and choice when it comes to their personal data with a powerfully automated privacy program from DataGrail. Companies can rely on privacy expertise and industry-leading customer support.

Start mitigating data risks today by reserving your 1:1 demo with DataGrail here.

Stay informed on the latest data privacy news and privacy regulations and insights with our newsletter.