How Do You Put a Price on Security and Compliance? Part IV – Minimizing Risk

This is the last in a four-part series of blogs on how we and our customers assess the business value of Protegrity solutions.

When it comes to approving expenditures on data security and governance solutions, C-suite executives look beyond technical specs to critical measurements like ROI and time-to-value. Based on the experiences of our customers, we have created three categories of spending justifications: reducing cost, increasing revenue, and minimizing risk. This blog will explore the third category: minimizing risk.

Caution: risk ahead

Some people embrace risk. Skydivers, rock climbers, and firefighters come to mind. How about business executives? Mmmmm, not so much. Most acknowledge that while some risks are inevitable, they want to minimize their impact.

In our age of cyberthreats, agentic AI, quantum computing, and digital everything, only enterprises that proactively and aggressively act to minimize risk have the best chance to avoid disaster.

AI’s insatiable appetite for data creates an entirely new imperative for safeguarding sensitive information to guard against data leaks and unauthorized access that could result in catastrophic consequences.

The costs of failing to minimize risk are numerous, including:

• Financial – fines, class-action settlements, customer compensation, insurance premiums, legal and IT expenses, ransomware payments.
• Reputational – loss of trust, weakened brand, lessened desirability as a vendor, employer or business partner, lower stock price/valuation.
• Operational – IT, security, legal, financial, and PR teams diverted from normal activities; disruption/downtime of core systems; overcompensation on controlling data access; weakened employee loyalty/commitment.

As noted in the second blog in this series, the cost of a major data breach or governance violation can exceed US$1 billion. And even if the direct cost isn’t that high, an organization still suffers from diverted attention and resources.

Risk assessment is a data challenge

Data security and regulatory compliance have always been priorities, of course, but the challenges are multiplying. We’re all familiar with HIPAA and GDPR. But how much do you know about SEC S-P, PCI DSS, NIST 800-53, FISMA, FedRAMP, DORA … OK, you get the point.

And then there’s the elephant in the room: agentic AI. It’s exponentially raising the risk of data leakage and governance violations.

To cope with the mounting challenges, many enterprises now have a chief risk officer (CRO), whose job is to anticipate and guard against threats to the health of the organization from numerous vectors.

Protecting the use of sensitive data by employees is high on a CRO’s agenda. But have you considered this: the office of CRO itself is a big consumer of data.

Take the example of the CRO at a large financial-services company. One of their major tasks is to gauge the risk of customer defaults. Doing so means pulling and analyzing data from numerous sources, such as PIIs, transactions, credit scores, market trends, customer support, and branch operations. These datasets reside in multiple silos controlled by different owners with various levels of access control. So, just amassing the data itself is a big lift, even before the analysis begins.

But default prediction isn’t simply a modeling issue; it’s also a data-access issue. The warning signs of a potential wave of defaults may exist, but the analytic team can’t see them if they don’t have the numbers in front of them.

Another example: Businesses are deploying AI models faster than the CRO’s team can validate them. And validation requires access to the training data, production data, and the outputs from the model. As in the previous example, this effort requires pulling data from multiple sources controlled by various owners, who sometimes may be in other countries. Moreover, every step of the validation process must be auditable and explainable.

Risk reduction isn’t just about security. It also demands auditability, governance, and enforcement. New rules such as DORA and the EU AI Act don’t just require an organization to manage risk; they require it to prove that the data behind its conclusions has been properly handled throughout the analytic process.

Protegrity’s approach to reducing risk

So, what’s the role of Protegrity in this environment?

As we just observed, risk assessment and mitigation is at root a data issue. And Protegrity is the global leader in assuring enterprises that all their sensitive data is protected and all relevant governance measures are implemented – automatically and persistently – no matter how complex the environment.

That’s because security and governance measures travel with the data itself, transparently, eliminating opportunities for malevolent actors to gain access or for trusted employees to inadvertently leak sensitive information. Authorized users are saved the time-consuming, often repetitive, tasks of accessing the data they need and ensuring that corporate, local, and international governance rules are followed.

Advanced techniques – including tokenization with referential integrity, dynamic data masking, and policy-driven access with audit logging – ensure that every workload run on a Protegrity platform satisfies the highest level of scrutiny on all four essential tests: security, auditability, governance, and enforcement.

The savings – in time, money, aggravation, blame-shifting, and violations – can be significant. Bottom line: the larger and more complex the project, the greater the savings will be.