How to navigate breach recovery costs to slash inefficiencies

Cyber incidents continue to rise in frequency, sophistication, and scale, making breaches more expensive than ever before for companies and the insurance carriers that cover them. Costs associated with downtimes typically comprise much of that price tag, but inefficient breach recovery––particularly regulatory non-compliance––is emerging as another major contributor. For carriers covering breach recovery expenses—like forensic investigations, legal fees, regulatory fines, customer notifications, and losses—efficient data mining for timely breach response has never been more critical.

Mitigating financial risk: Why accurate and timely notification matters
Timely breach response is no longer just about damage control—it's a financial imperative. By ensuring timely breach response, carriers help limit business interruption, ransomware payments, and additional costs that can arise from delayed or inadequate responses––thereby driving down their own costs. Today, a key part of timely breach response includes meeting critical breach notification requirements. 

When a data breach occurs, companies are legally obligated to notify victims, regulatory bodies, and sometimes the media. Laws mandating disclosure of breaches involving sensitive personal information vary by jurisdiction, industry, and type of data (e.g., healthcare data vs. financial data). Failure to follow notification requirements can dramatically amplify breach costs and damage company reputation, in some cases resulting in multimillion dollar fines. 

Corporate legal teams help victim organizations navigate this complex regulatory landscape and notify all relevant parties on time and in the right way, but it's the carrier who covers the steep fees of these lawyers. 

Data mining: The cornerstone of effective breach notification
Before a single notification goes out, organizations must determine the scope of the attack––what data was exposed, which individuals were affected, and whether the attack is ongoing. As data estates grow more complex, traditional methods can't keep up. The solution? AI-powered data mining. 

Unlike outdated manual reviews, AI-driven solutions extract compromised data—like Social Security numbers or addresses, and other sensitive information—at lightning speed. This information allows organizations to then promptly inform the right individuals, agencies, and media––avoiding costly over- or underreporting. For carriers and claims managers, the efficiency of that forensics and extraction work directly impacts the speed and accuracy of the breach notification process, which, in turn, affects the financial liability carriers face.

Inefficient data mining can significantly compound recovery costs for companies and their carriers. Notification delays prolong the time during which individuals are exposed to identity theft or fraud, amplifying compliance and legal threats. Additionally, legal fees balloon when data mining engagements are delayed, leaving the carrier to pick up the tab. Efficient, well-executed data mining, however, minimizes these risks––which means that selecting the right data mining vendor is a critical inflection point in the recovery process. 

Overbilling: An unfortunate standard in data mining
Most of today's data mining vendors, however, exhibit some less-than-favorable patterns: opaque pricing, slow turnaround, inaccurate deliverables, and security vulnerabilities, which can drag out the process and increase costs. The primary driver contributing to this unreliable model––a model primed for overbilling––is manual review. 

Manual reviews fall short in accuracy compared to advanced, technology-driven methods for identifying and extracting compromised data, especially given the vast and varied data companies manage today. Most vendors outsource manual reviews to offshore teams that lack the contextual knowledge to parse nuanced data correctly. The result? Incomplete or inaccurate reports and compliance threats that exacerbate delays and inflate legal fees, sending legal teams scrambling to clean up the mess—on the insurer's dime.

Compared to traditional manual review, AI-powered data mining delivers results at unparalleled speed, free from the constraints of human limitations like reading time, sick days, challenging moments, or off-hours. The technology is always active, day and night, weekdays and weekends. In fact, while humans process text at about one page per minute, software can analyze a page nearly instantaneously. 

Technology can also scale in a way that human teams cannot. As data mining projects unfold, data stores typically expand to much larger-than-predicted file counts. For AI-powered data mining solutions, this expansion poses little challenge; machines process files instantly and without labor constraints. However, in manual reviews, growing file counts are likely to introduce new bottlenecks and cause significantly extended timelines.

To prevent costly delays, meet strict notification deadlines, and avoid regulatory penalties, victim organizations are often left with no choice but to pay for all unexpected fees that come their way once they sign-on an incident response vendor. Be it overtime manual labor fees and weekend charges for manual labor because data volumes ballooned, or for cleanup of low-quality reports, any myriad of unexpected items that come up are thrust upon victim organizations because they are desperate to get through the incident. 

Whether it's overtime fees and weekend charges for manual labor due to ballooning data volumes or the cleanup of low-quality reports, or simply waiting for results—unknowingly incurring costs such as distractions from business-as-usual operations, legal fees from extended meetings, and potential fines for delayed notifications— a myriad of unexpected costs is often imposed on victim organizations desperate to get through the incident.

Pricing opacity in phased pricing is a convenient mechanism for vendors to hold the client hostage in desperate times just to deliver basic results. This means added cost in overtime fees and added down time from business operations.  In this phased pricing scenario, company data is essentially held hostage (again) until a new kind of "ransom" is paid. 

In contrast, data mining solutions that employ AI and machine learning (AI/ML) as the first and dominant step in the review process typically deliver a fixed cost engagement, since the software easily can handle the increased demands of expanded file sizes––not only preventing delays, but also providing more cost predictability for carriers.  

The future of cyber breach notification: Tech-first solutions
Because of the many inefficiencies of manual review, carriers are increasingly turning to cyber vendors that leverage AI and automation to streamline breach recovery. By automating data mining and review, companies can quickly identify affected data and notify stakeholders promptly, reducing recovery time while avoiding additional fines and lawsuits. In fact, research shows AI can lower breach costs by as much as $2.2 million in some cases. 

Beyond more predictable pricing, tech-dominant review also eliminates the need for costly-error prone manual reviewers, reducing workforce expenses and training overhead. It also enables on-shore processing, conducted securely on-premises behind the victims own firewall and under the supervision of knowledgeable experts – an approach that is not only more secure, but also more conducive to today's hybrid data estates. While on-shore, on-premises engagements were once synonymous with higher costs, leveraging cutting-edge tools can significantly reduce data mining expenses.

AI-driven data mining generates highly accurate, customized reports tailored to the specific needs of legal counsel during the notification process. This eliminates the need for costly legal teams to reformat or verify reports, streamlining breach recovery and reducing legal fees subject to the carrier.

Pricing opacity in data mining is another unfortunate byproduct of manual review-dominant processes and phased pricing agreements, which unfortunately remain an industry standard. When vendors lack the tools to deliver accurate, timely reports, it's the carrier that bears the cost. That's why claims managers need to vet the technological capabilities of data mining and manual review vendors before accepting a communicated timeline.

Burden of cost risk mitigation: The advent of fixed pricing?
Given the unexpected costs and risks historically tied to phased pricing models, it's time to consider shifting the burden to vendors—much like the federal government has done with fixed pricing in procurement. While fixed-price models may seem costlier upfront, they offer clear, predictable costs and strict deadlines, transferring the risks of budget overruns and delays onto the data mining or manual review vendor.

Fixed pricing incentivizes data mining vendors to streamline operations, invest in R&D, and adopt state-of-the-art technology—since inefficiencies directly impact their bottom line. The result is higher-quality reviews for carriers and the companies they cover. Fixed-fee pricing also protects claims managers from escalating costs when file counts expand or document reviews drag on, while rewarding vendors when sensitive data volumes are smaller than expected. In the end, a fixed-fee model benefits both carriers and vendors, creating a more balanced, efficient partnership for data mining engagements.

Tech-first incident response where everyone is a winner
For insurers, the path forward is clear: prioritize cost-efficient, transparent and tech-dominant solutions to slash inefficiencies, prevent overbilling, and streamline breach recovery. Partnering with vendors that prioritize advanced AI for on-shore, on-premises operations and uphold transparent pricing empowers insurers and their clients to optimize breach recovery, cut costs, reduce impact, and restore customer trust effectively.

The future of breach recovery isn't just faster—it's smarter, leaner, and more secure. It's time for insurers to demand better.