Data risk management shouldn’t be relegated to a technical function. It is more closely related to business planning issues with systemic implications.

Every business relies on data for everything from financing and product development to customer relations, operations, and future planning.

However, more data creates more exposure across cloud tools, employee workflows, vendors, and internal systems. Each channel is a potential weak point susceptible to outside penetration, and mitigating those risks helps maintain business continuity.

Aligning risk with business objectives helps make smarter decisions about allocating time, talent, and resources. By understanding business goals and adopting risk-aware frameworks, leadership can connect cybersecurity decisions with business objectives. Integrating security across technology, processes, and people enables leaders to measure success with metrics that truly matter.

Why data risk should fall under business strategy

Regulatory scrutiny, customer expectations, and competitive pressures all depend on adequate data risk management. This affects the integrity (security, privacy, quality, accuracy, and availability) of information essential to business processes and decisions.

Every employee who touches data plays a part in protecting data integrity, and everyday actions like following policies and reporting anomalies are the first line of defence. Those actions are built into business strategies that play out in training and operations.

Recent numbers from IBM reveal that the global average cost of a data breach was $4.4 million in 2025, a 9% drop from the year before, largely due to faster identification and containment.

That is a direct result of business changes that mitigated data risk, including incidents such as cybersecurity breaches, compliance violations, and operational disruptions.

Making the connection: Data risk vs business objectives

Identify the processes that rely on accurate, protected, and available data, everything from billing, onboarding, vendors, payroll, product development, logistics, and reporting.

This is the baseline for mapping the data that supports each process, such as employee and customer information, payment data, contracts, finances, and more.

Each data set should be weighted based on its degree of sensitivity, business value, compliance, and operational impact. Some key questions include:

1. What practices depend on this data?
2. Who owns this data?
3. Who has access?
4. What is the impact if this data is lost or compromised?

Get instant clarity on data risk and security decisions when all data is tied to a process or outcome.

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Controls that reduce data risk without impacting business continuity

Effective security controls should do more than simply lock down information. That only promotes inefficiencies later. Controls should protect data and minimise exposure while enabling teams to work confidently and efficiently.

Keep in mind that there has to be a clear balance. Too many unnecessary controls, and the human component becomes your biggest risk, as they feel pressure to bypass controls to stay efficient. A few foundational elements of a strong data security program include:

Controlled Access: Access should be thoughtfully scoped and restricted to essential functions required for each person’s role and responsibilities. This limits exposure and potential misuse.

Multi-Factor Authentication (MFA): High-risk environments that manage administrative accounts, financial information, remote access, and sensitive data must implement MFA to limit data exposure.

Backups and Redundancies: Data backups and redundancies must be implemented and regularly tested to ensure processes are resilient and that data can be restored quickly in the event of a disruption.

Employee Training: Humans will always be a potential security risk, but with solid training, they can be the strongest defence. Training should equip employees to recognise phishing attempts, practise safe file sharing, use strong authentication processes, and ensure software is safe.

These are relatively simple but effective controls that align with how teams and processes actually operate, reducing business risk.

Measuring Data Risk Progress

Clear metrics are the only way to accurately measure whether risk is decreasing due to intentional business changes, or if the boxes are merely being checked but the outcomes haven’t improved.

Don’t just log the number of alerts. What you really need is mean time to detect (MTTD), mean time to respond (MTTR), how many high-risk vulnerabilities were fixed or contained, how many backups for critical systems have been penetration-tested, and how many data assets have changed ownership.

This will reveal the true state of data risk—i.e. your level of resilience. Otherwise, all you have are policies on paper, but nothing concrete in execution. Make sure your organisation is prepared.

AI in managing data risk

AI has changed how leaders view data risk and what policies and safeguards must be in place to meet new challenges. One of the biggest issues with AI systems is their reliance on data quality, access control, and governance.

If sensitive data is shared with unapproved tools, the business can lose track of it. On the other hand, if AI tools use inaccurate data, their outputs can lead to flawed decisions.

Head this off by clearly defining what tools are approved, what types of data employees can share with those tools, who has access, and how outputs will be reviewed and verified. Identify confidential information, customer data, and intellectual property.

The key to effective AI use for data risk management is strong oversight. The tool is only as reliable as the data and controls that support it. Make AI governance part of how your organisation approaches data risk management.

Protecting data isn’t enough: Manage data lifecycles

The threats to data aren’t always from the outside. Retaining data for too long and failing to remove irrelevant data are also culprits. A strong data lifecycle management strategy gives organisations greater control over information from creation through archiving.

Define clear rules for what data should be collected, where it should be stored, who should maintain it, and how long it should be retained. When it is time to clean house, define the timeline for archiving or securely deleting data, and document how the removal process is carried out.

Organisations that collect customer, employee, and financial data pose greater legal risk and must take steps to reduce it and maintain compliance with data protection standards.

Optimising data risk management is part of business growth

Cybersecurity is most effective when it supports business operations rather than operating in isolation. Connect data risk to business objectives to make better growth decisions.

Nazy Fouladirad

President, COO at Tevora |  + postsBio

Nazy Fouladirad is President and COO ofTevora, a global leading cybersecurity consultancy. She has dedicated her career to creating a more secure business and online environment for organizations across the country and world. She is passionate about serving her community and acts as a board member for a local nonprofit organization.