Survey: Most Companies Ill-Prepared for a Global Data Breach

by Michael Bruemmer 5 min read June 27, 2017

Most companies aren’t prepared to respond to a global data breach, and aren’t yet ready to comply with the European Union’s General Data Protection Regulation (GDPR), even though it takes effect in less than a year, according to the latest Ponemon Institute report sponsored by Experian® Data Breach Resolution.

Nearly a third of the 588 information security and compliance professionals interviewed for the survey said their organizations had no global incident response plan in place, and 38 percent have a single plan that’s applied around the world. Just 27 percent reported having separate plans at the country or regional level, but even those who had a plan weren’t confident about its efficacy.

The global scope of data breaches

The number of data breaches reached a record high in 2016 — 4,149 incidents in 102 countries around the world exposed more than 4.2 billion records, according to cybersecurity company Risk Based Security. Ponemon’s survey underscores the scope of global data breaches; 51 percent of respondents reported their companies experienced a global data breach in the past five years, and 56 percent of breached companies had more than one incident.

When the GDPR goes into effect in May 2018, any company that processes and/or holds the personal data of European Union consumers will be required to comply with the regulation, regardless of where the company is located. Failure to comply can lead to fines ranging from 2 percent to 4 percent of a company’s annual global turnover.

Despite the escalating risks of falling victim to a global data breach and the possible repercussions of not complying with the GDPR, Ponemon’s survey shows a widespread lack of preparedness among companies.

Levels of unpreparedness

When it comes to preventing and responding to a global data breach, and ensuring they comply with the GDPR’s strict notification rules, many survey respondents expressed significant shortfalls in preparedness:

  • Outdated and inadequate security solutions would hinder the ability of 49 percent to cope with a global data breach.
  • Just 40 percent of respondents felt confident their organizations’ security technologies would adequately protect information assets and IT infrastructures overseas, and only 39 percent said they had the right policies and procedures to do so.
  • Slightly more than a third thought their companies could successfully manage cultural differences and privacy and data security expectations in different areas of the world.

A majority of respondents (89 percent) predicted the GDPR will significantly affect their data protection practices, and 69 percent felt non-compliance would hinder their companies’ ability to do business globally. Yet only a quarter said their companies were ready to comply with the new regulation.

While most understand GDPR is something they need to worry about, many aren’t sure what to do. The survey reveals some companies may be feeling desperate enough about the looming regulation to take drastic measures; 34 percent said their preparations include closing operations in countries with high non-compliance rates.

Timely notification of regulators and EU citizens affected by a data breach is a key component of the GDPR, yet the majority of our survey respondents (69 percent) said they would have trouble meeting the time limitations. The GDPR requires breached companies to notify regulators within 72 hours of discovering a breach, and affected consumers “without undue delay.” Half of our survey respondents said they experienced a global breach that required notification of victims. Only 10 percent were able to do so within the GDPR’s 72-hour window; 38 percent reported notification took two to five months to complete.

Obstacles to preparedness

The years-long evolution of the GDPR, which will replace older regulations, is evidence that world governments are taking data breach risks seriously. Unfortunately, our study indicates not all C-suite decision-makers are as concerned about global data breach risks as they should be and their antipathy is impairing their organizations’ ability to prepare for a global data breach.

While the security professionals surveyed cited high-volume breaches (65 percent) and breaches involving high-value information (50 percent) as the data risks that concern them the most, only 30 percent said their organization’s C-suite was fully aware of the company’s compliance status. Further, just 38 percent said their executives viewed global data regulations as a top priority.

Technology limitations and lack of executive support are significant obstacles to preparedness and compliance, but they’re not the only ones. Additionally, survey respondents cited:

  • Reluctance to make needed comprehensive changes in business practices (60 percent)
  • Not enough budget to hire staff (37 percent)
  • Unrealistic demands from regulators/regulations (35 percent)
  • Not enough money for appropriate security technology (34 percent)
  • Lack of knowledge about global data breach response (29 percent)

What companies must do

Some survey respondents indicated their organizations are taking the right steps toward preparedness and compliance. They are putting in place security technologies to quickly detect a data breach (48 percent), have tested and proven response plans (44 percent), can quickly identify whether a breach will require notification (15 percent) and are prepared to notify regulators within 72 hours of breach discovery (13 percent).

However, many organizations could be doing more to prepare for a global data breach and to comply with the GDPR. Global data breach risks continue to increase in number, scope and impact, and the potential loss of business and financial impact of a breach could prove catastrophic for affected companies. With less than a year to go until the GDPR takes effect, any company that conducts business internationally needs to act now to ensure it will be ready to deal with a global data breach when it occurs.

Related Posts

How Consumer Vehicle Choices Are Shaping Automotive Loan Trends

Conversations about rising auto loan balances and higher monthly payments has often centered around increasing vehicle prices and elevated interest rates; and while those factors have undoubtedly played a role, another important piece of the puzzle is the type of vehicles consumers are choosing to purchase. According to Experian’s Automotive Consumer Trends Report: Q1 2026, consumers are continuing to opt for SUVs over other vehicle types, a trend that may be contributing to higher average loan amounts and monthly payments. SUVs accounted for 63.5% of all new retail vehicle registrations over the last 12 months, up from 62.8% a year ago. Additionally, more than 117 million SUVs were in operation across the United States in the first quarter of 2026, making up 42.2% of the market share. At the same time, traditional passenger cars continue to fall in share, coming in at 16.5%, a decrease from 18.4% last year. As consumers increasingly gravitate towards the larger vehicle segment, it reflects the ongoing desire for versatility, cargo capacity, and family-friendly functionality. Electrification’s growing role in consumer purchasing behavior Interestingly, electrified SUVs continue to gain traction, representing 27.7% of all new SUV registrations, these vehicles include battery-electric, hybrids, plug-in hybrids, and other alternative fuel types. Diving a bit deeper, the Tesla Model Y was the market share leader for new, retail electrified SUV registrations in the last 12 months, coming in at 15.8%. Rounding out the top five were Honda CR-V (9.6%), Toyota RAV4 (7.2%), Chevrolet Trax (7.2%), and Toyota Grand Highlander (3.4%). As model availability and familiarity with the electrification segment grows, the broader adoption of these vehicles are playing an increasingly important role in vehicle pricing and overall consumer demand. While average loan amounts and monthly payments are being driven by a combination of factors such as financing costs and consumer purchasing behavior, data in Q1 2026 demonstrates the continued interest in SUVs. This suggests that the industry’s shift toward larger vehicles is likely playing a meaningful role in today’s financing environment. To learn more about SUV insights, view the full Automotive Consumer Trends Report: Q1 2026 presentation.

Published: June 17, 2026 by Kirsten Von Busch
When New Data Impacts MBS Pricing: Student Loan Debt

In our previous post, we described the Current Second Lien Balance field, which is one of over 2,000 fields in the new Experian Mortgage Loan Performance (MLP) dataset. We showed that the Current Second Lien Balance field meets our three-pronged materiality standard for new data delivery: New: Provides information not available in existing datasets (i.e., orthogonal to currently available data). Material: Impacts a sizeable portion of the MBS universe. Significant: Differentiates collateral performance by a large enough margin to influence trading and risk management decisions. In this article, we discuss another field that satisfies the above criteria: Student Loan Balance.  We evaluate this field in the context of these criteria. First, however, we provide a summary of the MLP dataset and how it compares to standard GSE loan-level data available today. Standard GSE Data vs. Experian Mortgage Loan Performance (MLP) Data The MLP dataset contains thousands of fields describing mortgage performance from each borrower, loan, and property perspective, all refreshed monthly (including, amongst other things, new credit scores and refinance inquiry activity, loan performance, filed junior liens, and AVM values).  MLP differs from loan-level data provided byFreddie Mac, Fannie Mae, and Ginnie Mae, which the vast majority of market participants solely rely on, in a number of ways: Standard data provided by the GSEs and GNMA does not contain all the information necessary for accurate forecasting of mortgage prepayment and credit performance. Basic, critical fields like borrower’s current credit score and current junior liens on the property are missing. The new Mortgage Loan Performance (MLP) dataset from Experian contains borrower, loan, and property data fields covering the entire mortgage universe, including Agency, Non-Agency, and Esoteric mortgage products (CES, HELOC, Reverse), both securitized and non-securitized. MLP enables full three-dimensional (borrower + loan + property) tracking with persistent keys for borrower (before and after refinancing), loan (in securities/deals even after exit due to payoffs or buyouts, including before and after MSR sales), and property.  This enables end-to-end analysis of each borrower’s (and property’s) mortgage experience throughout their credit lifecycle. New, Material and Significant Field:  Student Loan Debt MLP contains a number of fields describing each mortgage borrower’s student debt load, including amounts in repayment, forbearance and collections; estimated interest rate, time remaining until forbearance expiration, and more. In the interest of simplicity, for this article we’ll focus on a single student loan-related field within MLP: Student Loans Balance, which is defined as the total balance on open non-deferred student trades reported in the last 3 months. Is Information Regarding Student Loans New to Markets? Standard loan-level data disclosed by the GSEs and GNMA contain no student-loan-specific fields. Theoretically, fields related to DTI at origination might capture some aspect of student loan debt. So, in the best case scenario for an investor relying solely on standard disclosure, a DTI value as of origination is provided -- yet is never updated as the loan seasons and the borrower’s debt and income change (see more here).  But in the case of federal student loan debt attached to mortgages originated from early 2020 to late 2023, the level of detail provided by disclosure may be even more unknown due to COVID-era repayment and reporting moratoriums. The student loan repayment moratorium was a temporary federal policy that paused required payments, set interest rates to 0%, and suspended collections on most federally-held student loans. The moratorium began in March 2020, with payments resuming in October 2023, making it approximately 3.5 years in duration—the longest consumer credit payment pause in U.S. history. (Source: NCUA ) During the moratorium, student loan-related debt loads may have been understated as federal loans were in a temporary state of $0 repayment.  As an alternative to leaving student loan debt completely out of DTI calculations, an imputed payment equal to only 0.50% of the outstanding balance was often used as a placeholder for a borrower’s DTI calculation. As a result, mortgages originated during the moratorium may have artificially low reported DTIs for borrowers with student loan debt, materially understating true post-moratorium debt .  Accordingly, prepayment risk for these loans is likely overstated in mainstream market models. Standard data only reports information related to the primary mortgage and does not include any details on the borrower’s other debts with the exception of DTI at origination, which is never updated throughout the life of the loan. In contrast, MLP provides a comprehensive view of the borrower’s full credit profile, including other obligations such as credit cards, mortgages on other properties, student loan balances, and much more. Is Student Loan debt material to the residential mortgage market? Approximately $11 trillion of residential mortgage loans were originated during the student loan payment moratorium (Source: Experian MLP Dataset), a period marked by historically low mortgage rates during the COVID era.  As discussed above, DTI data contained in standard market disclosure may be particularly inaccurate for these loans. As the Wall Street Journal recently reported, a new report from the Federal Reserve of New York shows a rise in student loan default rates by age group.  Student l Of today’s $13 trillion in outstanding mortgage debt, more than 10% of that debt ($1.5 trillion) is associated with borrowers who carry student loan debt.  For these borrowers, the average amount of student loan debt outstanding is approximately $50,000, versus a mortgage balance of approximately ~$289,000. In other words, the average student loan debt balance is almost 20% of the mortgage balance for the average borrower who carries both. For this set of borrowers, the average monthly payment is approximately $400 for student loan vs. approximately $2,200 for 1st lien mortgage—so that monthly student loan payments are a significant debt load, approximately 20% of the monthly mortgage payment.  (Source:  Experian MLP Dataset) Is the effect of student loan debt a significant driver of performance? Figure 1 illustrates prepayments by student loan balance for a sample of loans drawn from MLP. The chart illustrates that borrowers with larger student loan balances prepay much more slowly, likely because some are effectively locked out of refinancing once student loan payments resume due to elevated DTI. The debt-to-income (DTI) ratio calculated using actual student loan payments may be significantly higher than the DTI calculated during the moratorium, in some cases exceeding GSE eligibility thresholds. As illustrated in Figure 1, for in-the-money (ITM) collateral, the differential between loans with material student loan balances (greater than $200,000) and loans with no student debt can reach up to 5 CPR. Notably, even for out-of-the-money (OTM) collateral, loans with student debt prepay 1 to 3 CPR slower, likely reflecting reduced mobility due to tighter financing constraints when purchasing a new home. Pools with otherwise similar prepayment characteristics may exhibit different prepayment behavior depending on the distribution of student loan exposure within their collateral. In addition, because loans with student debt tend to prepay more slowly, this effect increases over time due to burnout: loans without student debt prepay and exit the pools more quickly, leaving a higher concentration of slower-paying loans behind.  Given that 10% of the $13 trillion outstanding mortgage market is associated with borrowers who have student loans (Source:  Experian MLP dataset)—and that student loans have a meaningful impact on prepayments—many pools issued between March 2020 and October 2023 may be subject to this student loan debt CPR throttle, and therefore mispriced by investors relying exclusively on standard market data. Fig 1. Prepayment S-Curve: Student Loans Balance Source:  Experian MLP dataset hosted on IVolatility Data-Driven Platform   _____________________________________________________ Michael Pyatski advises MBS traders, portfolio managers, quants, risk managers, loan originators, and technology professionals on making informed, data-driven business decisions that drive revenue growth, enhance risk management, and reduce trading costs. With more than 15 years of experience as an Agency RMBS trader—including serving as Head of the Proprietary Trading Desk at BNP Paribas—Michael developed and successfully implemented relative-value, data-driven profitable trading strategies to capture market opportunities embedded in data but not fully priced by the market. His trading experience, combined with a Ph.D. in econometrics, led him to found the Data-Driven Portal (https://datadrivenportal.com/), a platform that provides advanced technology for MBS trading and risk management. The platform’s No-Model Data-Driven technology leverages big data, econometric analysis, and AI to help traders identify relative-value opportunities in RMBS markets and generate above-market, risk-adjusted returns. _____________________________________________________

Published: June 17, 2026 by Perry DeFelice
Empowering merchants to reduce first-party fraud and chargebacks

Reduce first-party fraud and chargebacks with data-driven strategies that help merchants prevent disputes, protect revenue and improve customer trust.

Published: June 15, 2026 by Charles Hunter