Categories
Blogs

Is Your Data Safe in a Post-Quantum World?

Blogs

Could the marvels of quantum computing break our conventional data storage systems? Dive deep into how RPA is emerging as the guardian of quantum-resilient data storage.

The pace at which technology advances is mind-boggling. Once dominated by magnetic tapes, the data storage landscape has evolved into high-speed SSDs and sophisticated cloud architectures. But what happens when quantum computing, a technology that’s gaining rapid traction, poses a threat to our existing storage infrastructures?

Why Should You Care?

Whether in healthcare, financial services, pharmaceuticals, or agriculture, how data is stored matters more than you might think. Not only do these sectors have to maintain massive records, but they also have to protect data from malicious entities. Imagine a quantum computer, with its unparalleled computational capacity, being used by a bad actor to break into conventional storage systems. Scary, right?

Here’s the value bomb: The evolution of Robotic Process Automation (RPA) could be the answer to creating quantum-resilient storage systems. Let’s explore.

1. Healthcare: Digital EHR and Quantum-Resilient Storage
A renowned hospital dealing with millions of electronic health records (EHR) recently piloted an RPA-backed quantum-resilient storage system. The results? Not only did the RPA help seamlessly transfer EHRs to the new quantum-secure system, but also detected and rectified inconsistencies in the process.
Do you belong to the healthcare industry? How do you envision the role of RPA in ensuring the security of patient data?
2. Financial Services: Protecting Transactions in the Quantum Age

A global bank incorporated RPA to transfer its vast trove of transaction data to a quantum-resilient platform. With the potential vulnerabilities of quantum hacks, the bank’s proactive approach, assisted by RPA, ensured a smooth transition without compromising transaction integrity.

How do you think financial institutions should prepare for the inevitable rise of quantum computing?

3. Pharmaceuticals: Securing Formulae and Research Data
A leading pharmaceutical company, sitting on years of research data, realized the impending threat of quantum computing. Using RPA, they migrated their invaluable data to a quantum-secure system. The automation facilitated by RPA ensured that the intricate data structures and formulae remained intact during the transition.
Have you thought about how RPA might revolutionize data handling in the pharmaceutical domain?
4. Agriculture: Shielding Genetic Data for the Future
With the agriculture industry increasingly relying on genetic data, one pioneering agri-firm leveraged RPA to transfer its genome databases to a quantum-resilient system. The migration was efficient, and RPA also automated periodic checks to ensure data accuracy. Considering the crucial role of data in modern agriculture, how do you see its protection evolving in the coming years?
In Conclusion
The threat posed by quantum computing to our existing data storage systems is real. But, as with every technological challenge, innovative solutions like RPA are rising to meet the challenge head-on. By examining these case studies, we can see the potential of RPA in safeguarding our data in a quantum-dominated future.
I have a question for you: Are you prepared for the quantum shift? How do you see RPA shaping the future of your industry’s data storage needs?

Take the Leap: If you’re as fascinated by this as we are and keen to dive deeper into the world of quantum-resilient storage, contact us. Equip yourself with the knowledge and tools to stay ahead in this brave new quantum world.

Share News

Categories
Blogs

Revolutionizing Record Management: Generative AI’s Role in Highly Regulated Industries

Blogs

In today’s fast-paced world, industries such as financial services, healthcare, pharmaceuticals, manufacturing, and agriculture are grappling with an ever-growing volume of regulated records. From financial transactions and patient data to drug development and quality control, these sectors are inundated with information that must be managed meticulously to meet compliance requirements. Enter Generative AI, a game-changing technology that has the potential to revolutionize record management in these highly regulated industries.

The Regulatory Challenge
Before we delve into how Generative AI can address the record management challenges in these sectors, it’s crucial to understand the regulatory landscape in which they operate. Industries like finance and healthcare are subject to many stringent regulations and standards, such as HIPAA, GDPR, Sarbanes-Oxley, etc. These regulations impose strict requirements on data storage, retrieval, security, and auditing, making record management a formidable task.
Additionally, the pharmaceutical, manufacturing, and agriculture industries must adhere to Good Manufacturing Practices (GMP), Good Laboratory Practices (GLP), and Good Documentation Practices (GDP), which necessitate precise recordkeeping throughout the product lifecycle. Any non-compliance can result in severe consequences, including hefty fines, legal liabilities, and damage to reputation.
Generative AI: A Game Changer

Generative AI, powered by deep learning algorithms, has the potential to alleviate the record management burden in these industries. Here’s how:

1. Data Extraction and Classification
In financial services, healthcare, and other sectors, a vast amount of data is generated daily, often in unstructured formats like documents, emails, and handwritten notes. Generative AI can automatically extract and classify relevant information from these unstructured sources.
For instance, in healthcare, Generative AI can scan medical records and automatically identify and categorize patient data, ensuring that sensitive information is handled with utmost care. Similarly, AI can extract transaction details in financial services, enabling efficient auditing and compliance checks.
2. Error Reduction and Quality Assurance
Maintaining meticulous records in pharmaceuticals, manufacturing, and agriculture is critical to ensuring product quality and safety. Generative AI can play a pivotal role in error reduction and quality assurance by automating data entry and validation processes.
These industries can minimize human errors by utilizing AI-powered algorithms, thereby decreasing the risk of non-compliance with regulatory requirements. This not only improves operational efficiency but also enhances product quality and safety.
3. Predictive Analytics and Risk Management
One of the most promising applications of Generative AI in highly regulated industries is its ability to perform predictive analytics and risk assessments. In finance, AI can analyze historical transaction data to detect anomalies or suspicious activities, facilitating fraud detection and risk mitigation.
Generative AI can predict patient outcomes and identify potential health risks by analyzing vast datasets in healthcare. Pharmaceutical companies can use AI to predict the success of drug candidates and optimize clinical trial designs, ultimately expediting drug development.
Managing legacy applications is a task that requires careful consideration and strategic planning. By addressing the challenges head-on, application owners can ensure that these critical systems continue to serve the business effectively. Modernizing the technology stack in stages, prioritizing thorough documentation, implementing robust security measures, following integration best practices, and investing in skill development are all integral components of a successful strategy.
4. Audit and Compliance Tracking
Maintaining a comprehensive audit trail is a cornerstone of regulatory compliance. Generative AI can automate tracking changes and access to records, ensuring a transparent and auditable record management process.
By providing a detailed and real-time view of record modifications and access, Generative AI helps organizations demonstrate their commitment to compliance, making audits smoother and more efficient.
5. Scalability and Cost Reduction
As these industries grow, the volume of regulated records will only increase. Traditional record management processes can become cumbersome and costly. Generative AI offers scalability, allowing organizations to efficiently manage large volumes of records without a proportional increase in labor costs.
By automating many record management tasks, Generative AI reduces operational expenses and frees up human resources to focus on higher-value activities such as innovation and customer service.
Challenges and Considerations
While Generative AI holds immense promise in revolutionizing record management in highly regulated industries, there are challenges to consider:

1. Data Privacy and Security: Handling sensitive data is a significant concern. Organizations must ensure that Generative AI systems are robustly designed to protect data privacy and adhere to regulatory requirements.

2. Training and Expertise: Implementing Generative AI requires expertise in AI technology and domain-specific knowledge. Companies may need to invest in staff training or seek external partnerships to maximize the benefits.

3. Ethical Considerations: The use of AI in record management raises ethical questions about data bias, transparency, and accountability. Organizations must adopt ethical AI principles and practices.

Conclusion
Generative AI is poised to transform record management in highly regulated industries, enabling organizations to navigate complex regulations more efficiently and effectively. By automating data extraction, error reduction, predictive analytics, audit tracking, and scalability, Generative AI offers a comprehensive solution to the record management challenges these industries face.
As these industries continue to evolve and grow, embracing Generative AI will ensure regulatory compliance and foster innovation and competitiveness. It’s time for financial services, healthcare, pharmaceuticals, manufacturing, and agriculture to recognize the potential of Generative AI and embark on a transformative journey toward more efficient and compliant record management practices. In doing so, they can unlock new opportunities and stay ahead in an increasingly regulated world.

Share News

Categories
Blogs

Navigating Legacy Application Management: Challenges and Solutions Introduction

Blogs

In today's fast-paced technological landscape, managing legacy applications within a large company poses unique challenges. While these applications have been the backbone of business operations for years, they often present hurdles that impede efficiency, security, and innovation. We’ve interviewed 20 Application Owners from banks, global real estate companies, healthcare organizations, and cyber security experts, so let's dive into the top five challenges application owners face when managing legacy systems and outline practical strategies to overcome them.
Challenge 1: Outdated Technology Stack
An outdated technology stack is one of the most common challenges in managing legacy applications. These applications were built using technologies that were cutting-edge at the time but have since become obsolete. As a result, integrating new features, scaling to meet increased demands, and maintaining security can be arduous tasks.

Solution: Embrace a phased modernization strategy. Gradually migrate application components to more contemporary technology, like the Infobelt Omni Archive Manager’s Application Retirement Module. Adopt a microservices architecture to modularize the application and facilitate incremental upgrades. Leverage containerization technologies like Docker to streamline deployment processes. Modernizing in stages can maintain business continuity while reaping the benefits of newer frameworks and tools.

Challenge 2: Lack of Documentation
Legacy applications often need more comprehensive documentation. This documentation deficit hampers troubleshooting, knowledge transfer, and understanding the application’s intricacies.

Solution: Prioritize documentation as an ongoing effort. Create clear architectural diagrams illustrating the application’s structure and data flows. Encourage developers to provide detailed code comments explaining complex codebase sections. Additionally, develop user guides that describe the application’s functionality from an end-user perspective. Regularly update documentation as the application evolves, ensuring it remains an invaluable resource for your development team.

Challenge 3: Security Risks
Maintaining security in legacy applications is a formidable challenge due to outdated libraries, unsupported components, and inadequate security measures that were acceptable in the past but are now vulnerable to modern threats.

Solution: Implement a robust security regimen. Regularly conduct code reviews to identify and remediate vulnerabilities. Perform regular vulnerability scans and penetration tests to identify potential weaknesses. Address identified vulnerabilities promptly and apply security patches as needed. If modernizing the application is impractical, consider adding additional security layers, such as a Web Application Firewall (WAF) or intrusion detection systems, to fortify your defenses.

Challenge 4: Integration Issues
Integrating legacy applications with newer systems can be complex and fraught with difficulties. The potential for data inconsistencies and operational disruptions is significant.

Solution: Employ integration best practices. Utilize middleware or integration platforms to facilitate seamless data exchange between legacy and modern systems. APIs, microservices, and Enterprise Service Buses (ESBs) can help abstract integration complexities, allowing systems to communicate effectively without direct dependencies. Decoupling integrations from the core application minimizes the risk of disruptions caused by changes in one system affecting others.

Challenge 5: Talent Shortage
Locating skilled developers who possess knowledge of older technologies and languages used in legacy applications can take time and effort.

Solution: Invest in skill development. Encourage your existing development team to learn about the legacy technologies underpinning the application. Facilitate cross-training to bridge the knowledge gap and empower your team to manage the application effectively. Additionally, consider outsourcing specialized tasks to experts well-versed in legacy systems. Collaborating with external consultants or partnering with companies specializing in legacy application support can alleviate the pressure of talent shortage.
Conclusion

Managing legacy applications is a task that requires careful consideration and strategic planning. By addressing the challenges head-on, application owners can ensure that these critical systems continue to serve the business effectively. Modernizing the technology stack in stages, prioritizing thorough documentation, implementing robust security measures, following integration best practices, and investing in skill development are all integral components of a successful strategy.

As we navigate the complexities of legacy application management, remember that the journey is not without obstacles. However, these challenges can be overcome with a proactive approach and a commitment to continuous improvement. By leveraging the right solutions, like the Infobelt Omni Archive Manager’s Application Retirement Module, application owners can transform their legacy systems into resilient, secure, and valuable assets that contribute to the company’s growth and success in the modern era of technology.

Share News

Categories
Blogs

The Future of Artificial Intelligence in Data Preservation and Business Records Management

Blogs

The world is experiencing an unprecedented explosion of data, with businesses generating massive amounts of information daily. Efficiently managing and preserving this data has become a paramount challenge for enterprises seeking a competitive edge in the digital age. Enter Artificial Intelligence (AI), a technology that promises to revolutionize data preservation and business records management. In this blog post, we will explore how AI is shaping the future of data preservation and transforming the landscape of business records management.
1. AI-Driven Data Preservation
Data preservation safeguards valuable information to ensure its longevity and accessibility over time. As the volume and complexity of data continue to increase, traditional data preservation methods need to be revised. However, AI is changing the game by offering intelligent solutions enabling businesses to manage their data preservation needs proactively. Machine Learning (ML) algorithms can analyze patterns in data usage, predict potential issues, and optimize storage, ensuring critical information is safeguarded against loss. Additionally, AI-driven data preservation systems can automatically detect and repair corrupted files, reducing the risk of data degradation over time. By leveraging AI, businesses can achieve cost-effective, scalable, and reliable data preservation, supporting the seamless functioning of organizations across industries.
2. Enhanced Data Classification and Organization
Effective business records management requires precise data classification and organization. Traditionally, this task has been labor-intensive and error-prone. However, AI-powered data classification tools can analyze vast amounts of unstructured data and accurately categorize it based on predefined parameters. Natural Language Processing (NLP) algorithms can extract critical information from text-based records, such as contracts, invoices, and legal documents. Image recognition capabilities can help classify visual data, including scanned documents and images. These AI-driven tools streamline records management processes, enabling businesses to quickly locate and retrieve essential information, resulting in improved operational efficiency and compliance.
3. Intelligent Data Retention Policies
Developing data retention policies that comply with legal and regulatory requirements can be complex. Failure to adhere to these policies can lead to severe consequences, including fines and reputational damage. AI can assist in crafting intelligent data retention policies that automatically adapt to changing regulations and business needs. By analyzing historical data usage patterns and monitoring regulatory updates, AI systems can recommend appropriate retention periods for different types of records. As a result, businesses can strike a balance between retaining valuable data for historical analysis and disposing of obsolete information in a compliant manner.
4. Predictive Analytics for Better Decision-Making
AI-driven predictive analytics transforms how businesses make decisions by providing valuable insights based on historical data and real-time inputs. By analyzing records and detecting trends, AI can forecast potential risks and opportunities, aiding businesses in strategic planning and risk management. Moreover, AI-powered analytics can identify anomalies in financial records, supply chain operations, and customer behavior, thereby mitigating the impact of fraud and irregularities. These proactive measures can prevent financial losses and protect an organization’s reputation.
5. Automation of Records Management Processes
AI-driven automation is at the core of the future of records management. Repetitive and time-consuming tasks such as data entry, document indexing, and content retrieval can be efficiently handled by AI-powered robotic process automation (RPA). RPA bots can interact with multiple systems and applications, ensuring seamless integration across various data sources. This level of automation saves time and resources and minimizes human errors, resulting in increased data accuracy and compliance.
6. Data Privacy and Security
Data privacy and security are paramount concerns for businesses dealing with sensitive information. AI plays a crucial role in bolstering data protection measures. AI algorithms can continuously monitor networks for potential threats and quickly respond to security breaches. Additionally, AI-powered encryption techniques can safeguard data at rest and in transit. By analyzing user behavior patterns, AI can detect suspicious activities and enforce access controls, reducing the risk of unauthorized data breaches.
The future of artificial intelligence in data preservation and business records management holds immense promise for businesses seeking to thrive in a data-driven world. AI’s capabilities, such as data preservation, enhanced data classification, intelligent retention policies, predictive analytics, automation, and improved data privacy and security, offer significant efficiency, compliance, and strategic decision-making advantages. As AI technology advances, businesses must embrace these transformative solutions to stay ahead in the competitive landscape. By leveraging AI’s potential, enterprises can unlock new possibilities for data preservation and records management, setting the stage for a more intelligent and prosperous future.
Join our community of avid readers and stay informed! Subscribe to our monthly newsletter now and receive exclusive content straight to your inbox.

Share News

Categories
Blogs

Archiving Your Way to Better Data Management and Security

Blogs

In the current digital era, businesses generate and accumulate vast amounts of data daily. The influx of information poses significant challenges for data management and security. However, organizations increasingly recognize the importance of archiving to overcome these challenges. Archiving is a proactive approach that streamlines data storage and enhances data management and security. In this blog post, we will explore why archiving is crucial for businesses and how it can assist in achieving better data management and security.
Safeguarding valuable data is pivotal for businesses, and archiving plays a crucial role. By systematically archiving data, companies can ensure the long-term preservation of critical information, even as technology evolves. This protection is vital for compliance, litigation, and historical records. Archiving prevents data loss due to accidental deletions, hardware failures, or cyberattacks, providing businesses with peace of mind and protection against potential disruptions.
Archiving also helps businesses improve their data management practices. Organizations can reduce their primary storage infrastructure strain by implementing archiving systems. Infrequently accessed data can be moved to lower-cost storage mediums, freeing up valuable space on high-performance storage systems. This optimization enhances storage efficiency and reduces the costs of acquiring additional storage resources. Archiving also simplifies data retrieval and improves search capabilities, allowing employees to access relevant information quickly.
Compliance with various regulations is a significant concern for businesses across industries. Archiving facilitates compliance efforts by ensuring that essential records and documents are securely stored and readily accessible when needed. Regulatory bodies often require organizations to retain data for specific periods, and archiving assists in meeting these requirements. By maintaining a comprehensive and well-organized archive, businesses can easily retrieve relevant data during audits or legal proceedings, avoiding potential penalties and reputational damage.
A robust archiving strategy can be crucial for legal disputes or litigation businesses. Archived data can serve as essential evidence and support in legal proceedings, helping organizations defend their interests. Companies can demonstrate data integrity, establish timelines, and support their legal claims by maintaining accurate and tamper-proof archives. Archiving also helps mitigate the risk of spoliation, which refers to the intentional or accidental destruction of evidence. Consistent archiving practices ensure that data remains intact and unaltered, strengthening a business’s legal position.
Let’s explore the steps to automate archiving and discuss how it can help businesses achieve better data management and security.
  1. Assess Your Archiving Requirements: The first step in automating archiving is to assess your organization’s specific archiving requirements. Consider the types of data you need to archive, regulatory compliance obligations, retention periods, and access requirements. By identifying these factors, you can design an archiving solution that aligns with your business needs and ensures that the appropriate data is archived.
  2. Choose an Archiving Solution: Select a suitable archiving solution that supports automation features. Look for features such as automated data classification, policy-based archiving, and integration capabilities with existing systems. An archiving solution should provide scalability, robust security measures, and flexibility to adapt to future data volumes and format changes.
  3. Implement Data Classification: Data classification is essential for effective archiving automation. Define classification rules based on data types, sensitivity, and relevance. Automated classification tools can scan data and assign appropriate tags or metadata to facilitate archiving. This step streamlines the archiving process by automatically identifying which data should be archived, improving efficiency and accuracy.
  4. Define Archiving Policies: Establish archiving policies that dictate when and how data should be archived. These policies can be based on data age, usage patterns, or specific business requirements. Automated archiving solutions enable the creation of rules and triggers that initiate the archiving process based on predefined criteria. Archiving policies ensure consistency, reduce manual intervention, and allow timely data archiving.
  5. Set up Regular Archiving Schedules: Automation allows businesses to set up schedules based on their specific needs. Define intervals or triggers that initiate archiving processes automatically. For example, you can schedule archiving weekly, monthly, or when data reaches a certain threshold. Regular archiving ensures that data is consistently managed and archived promptly, minimizing the risk of data loss or non-compliance.
  6. Integrate with Existing Systems: To achieve seamless automation, integrate your archiving solution with existing systems and applications. This integration enables data extraction, transformation, and archiving without disrupting daily operations. Integration also facilitates data retrieval, ensuring that archived data remains easily accessible for authorized personnel.
  7. Implement Security Measures: Automation should be accompanied by robust security measures to protect archived data. Implement encryption techniques to secure data during storage and transmission. Apply access controls and authentication mechanisms to restrict unauthorized access to archived data. Regularly monitor and update security measures to stay ahead of evolving threats.
  8. Monitor and Maintain Archiving Processes: Regularly monitor and maintain your automated archiving processes. Keep track of archiving logs, verify data integrity, and address any issues promptly. Conduct periodic reviews to ensure archiving policies align with business requirements and regulatory changes. Proactively maintaining the archiving system can enhance data management and security over time.
Data breaches and cyber threats pose significant risks to businesses in the digital landscape. Automating archiving processes offers numerous benefits to businesses, including improved data management and enhanced security. Organizations can achieve efficient archiving workflows by assessing archiving requirements, selecting a suitable solution, implementing data classification, defining archiving policies, establishing regular schedules, integrating with existing systems, and implementing robust security measures. Automation streamlines data archiving reduces manual effort, ensures compliance, enhances data security, and enables businesses to focus on core operations. Embracing archiving automation empowers organizations to achieve better data management and security in today’s data-driven business landscape.

Share News

Categories
Blogs

What is Regulatory Compliance Risk? And How Does Data Archiving Help?

What is Regulatory Compliance Risk? And How Does
Data Archiving Help?

By virtue of the type and volume of data they manage, financial services companies take on significant regulatory compliance risk. Compliance officers face the daunting task of understanding and managing adherence to an ever-increasing number of complicated laws and regulations. A veritable acronym soup of regulations include rules for both data privacy and data retention–and the risks corresponding to these mandates can often seem to be at odds with each other.
Given the rapidly changing regulatory landscape, it can be difficult to understand regulatory compliance requirements to accurately assess and manage the associated risk. Let’s take a step back and start with the basics and explore how solutions like data archiving can help.
What is Regulatory Compliance Risk?
Regulatory compliance is an overarching term that refers to an organization’s practice of following the laws and regulations that govern its business.
Regulatory compliance risk is, simply, the chance that your organization might break one of the laws that regulates how it does business and be penalized for doing so.
Regulations can be specific to both the industry and the jurisdiction in which a company does business. Some 128 countries have data privacy laws; many of these regulations only came into being within the last five years and often apply to companies within and outside their geographical area. For instance, consider the well-known GDPR legislation: These stringent data protection rules cover not just European companies but any organization that does business or has customers in the EU.
Companies in the financial services (finserv) industry are hit with a double-whammy of sorts when it comes to regulatory compliance. First, of course, they move massive amounts of money. With that comes massive volumes of sensitive customer data that is generated—and subsequently stored—on a daily basis. These attributes combine to make finserv firms a flashing target for cyber criminals and hackers. As such, these companies are subject to a rapidly growing number of regulations established to both protect consumer rights and prevent damage to the global economy that could result from a security breach.
And of course, with these regulations comes significant risk to organizations scrambling to understand and comply with them.
Regulatory Compliance Risk in Financial Services
Regulatory compliance risk in the finserv industry is complicated not just by the volume of data that is managed—and that volume is tremendous—but also by the type of data used by this industry. Whether a firm is small or large, chances are it’s dealing with myriad types of sensitive customer and employee data:
  • Personal customer data (name, address, birthdate, Social Security number, etc.)
  • Credit information
  • Mortgage and loan information
  • Transaction details
  • Email and other logged communications
  • Personal employee information and salary information
  • Analytics and marketing data
  • And more
To complicate matters even further, these different types of data are typically stored in different formats on different systems, all with varying levels of security. Considering that all of this information is sensitive and simultaneously subject to a number of different regulations, the compliance risk associated with the variety of data and systems is substantial.
The Cost of Regulatory Compliance in the Real World
No matter how you slice it, maintaining regulatory compliance is expensive.
With the increasing prevalence of cyber security threats, firms large and small have been forced to make significant investments in both human and technology resources to adequately monitor and manage the risk associated with non-compliance. The work of compliance officers and their teams is more important than ever for executing effective strategies to identify and mitigate risk. At the same time, software solutions have evolved to provide automated tools for managing regulated and unregulated information at scale.
Though these investments are substantial, failing to meet compliance requirements has been reported to be nearly three times more costly. According to figures from the Association for Intelligent Information Management, the average cost of compliance for all organizations in a 2017 study was $5.47 million, while the average cost of non-compliance was $14.82 million. Harkening back to the GDPR example, fines start at $11 million or 2% of annual revenue for compliance violations.
And it’s not just huge, international corporations that are subject to regulatory compliance risk. Since all finserv companies handle similar types of regulated data, they are all subject to scrutiny and costly repercussions when not in compliance.
Expenses associated with non-compliance accumulate not only with the fines and penalties associated with breaking regulations, but also with lasting costs like damage to customer trust, loss of investor confidence, diminished employee morale, and hits to corporate reputation.
Compliance Strategy: Data Archiving
One of the ways to reduce data compliance risk is efficient implementation of data retention policies and systems to monitor their implementation and enforcement. Unfortunately, this can present a herculean task for compliance teams dealing with the volumes–and wide variety–of sensitive data in the financial services industry.
This is where software solutions can help. From a technology perspective, there are two approaches to managing the mountains of private data that must be retained: backups and archives. While both approaches store data, they were created for different purposes.
A backup makes a copy of all data so that, should that data become damaged, corrupted, or missing, it can be recovered quickly. Backups are important for ensuring business continuity, for instance, to restore a database to a last-known-good state following a software or hardware failure. However, the storage space and costs associated with backups are significant. Given the vast quantities of data produced in a finserv company in a single day, backups are not a long term solution for compliance-related data retention.
The process of data archiving, on the other hand, handles inactive or historical data. Archiving stores a copy of this data for legal or compliance reasons. Archiving inactive data is more efficient than straight back-ups, freeing up storage space and bandwidth for current transactions.
In addition to freeing up valuable and expensive storage space, the data archiving approach meets additional requirements for reducing regulatory compliance risk:
Immutable Storage. An important aspect of data retention regulations is that data be stored in an unalterable state. Data archiving solutions use WORM (write once, read many) storage to ensure that data is immutable. In a WORM system, data cannot be changed, overwritten, or deleted, even by the administrator. The same cannot be guaranteed by backups alone.
Access tracking. Archiving provides a granular level of detail about who accesses the data and when, which is required for audits as well as for analyzing any security incidents.
Scheduled destruction. Once data is no longer required for regulatory compliance purposes, it can be destroyed to free up space. Destroying unneeded data also removes the risk of it becoming compromised. A data archive solution should have scheduled data destruction built in, removing this task from the compliance officer’s plate.
Management of disparate data. A data archiving solution that can handle different types of data efficiently is an absolute must for finserv companies that transact structured and unstructured data from various systems.
Get Started with Data Archiving
Interested in how a data archiving solution can help take the headache out of managing regulatory compliance risk? Take a look at our Omni Archive Manager, or reach out to talk to one of our specialists.

Share News

Categories
Blogs

Six Reasons Why Applications Need To Be Retired (and Why It Saves, If Done Correctly)

Six Reasons Why Applications Need To Be Retired (and Why It Saves, If Done Correctly)

Why do applications need to “retire”?
Anyone who runs an IT department will tell you that, over time, applications become redundant (or even obsolete). When this happens, one or more applications become underused…or cease to be used at all. Those unused applications can create severe problems, especially for highly regulated industries.
For example, redundant applications are frequently a result of:
  • Mergers and acquisitions
  • Product lines or services being discontinued
  • Departments being disbanded
  • Other assets/business lines being divested
  • Applications being replaced with more up-to-date alternatives
So applications become outdated and go unused…but so what? Can’t the application simply remain as-is, just in case it or some of its data are needed?
Generally, this is a bad idea. There are several reasons why these legacy applications need to be retired appropriately and not left to linger on systems.
Reason #1: They Are Business Risks
The technical skills required to maintain a legacy system are often in short supply. For example, between 2012 and 2017, nearly 23% of the workforce with knowledge of mainframes retired or left the field, according to Deloitte. Finding people with legacy tech skills can be costly, and keeping them in-house can be difficult. The Deloitte study also found that 63% of those “legacy mainframe” positions remained unfilled at the time of the study.
Many legacy applications are also incompatible with more current systems and software. Thus, legacy systems might only work with older operating systems and databases, which themselves have not been updated with the latest security patches or software updates. This is both a stability issue and a cybersecurity risk.
Reason #2: They Are Costly
Gartner estimates that the annual cost of owning and managing software applications can be as much as four times the cost of the initial purchase, with 75% of their total IT budget spent on maintaining existing systems and infrastructure. In some instances, software vendors will charge more for supporting older versions. IT personnel’s extra time resolving problems associated with less-familiar systems can also create high support costs.
Reason #3: They Raise Regulatory Compliance Concerns
Around the world, there is rising concern about data governance. Regulations such as SEC 17a3/4, FINRA 4511, GDPR, and many other government mandates have forced most companies to pay closer attention to managing data and protecting data privacy. Older applications may not provide the security levels required to control sensitive data access and may be incompatible with modern access requirements.
Businesses must also balance the two priorities of data minimization and compliance with long-term retention requirements. A legacy application typically lacks the necessary controls to meet these requirements. In contrast, a purpose-built application retirement repository will incorporate data lifecycle management capabilities to handle things like data retention, data destruction at the end of life, eDiscovery, and legal holds.
Reason #4: They Suck Up Time and Talent That Could be Spent on Innovation
Supporting legacy systems is a distraction from modern business and IT initiatives. Retiring legacy applications not only frees IT personnel from firefighting problems on systems that have little value to the company, but it also reduces the overhead needed while allowing the IT team to focus its energy on innovation.
Reason #5: They Can Devalue the Customer Experience
Legacy systems are often isolated from other pieces of customer data, which means that customer requests can be slower and less efficient—especially if customer service teams need to log into multiple systems to access customer information. On the other hand, a single content repository for legacy and current application data provides secure access to all information in one place.
Reason #6: They Are a Lost Opportunity for Business Insights
Most organizations have a mountain of operational and customer data hiding in legacy systems. That data could deliver valuable business intelligence…but only if it is accessible in the right ways. Decommissioning or retiring an application offers a way to bring together diverse information from disparate systems into a single location. Once combined, the data can be mined using analysis tools or interrogated using artificial intelligence.
But Is There an ROI for Application Retirement Solutions?
Naturally, there is no general answer to this question. Whether or not your organization can benefit from an application retirement solution depends on the number and scope of its legacy applications, its exposure to risks around data retention and compliance, its current spend on these applications, and a number of other factors.
One way to begin that ROI calculation is to consider four categories of potential savings:
  • Direct savings. This is the money saved through elimination of legacy support and maintenance tools and services.
  • Efficiency gains. These would include efficiencies that evolve when business users have access to all data in a central place. This would include increased efficiency for customer service. (See Reason #5.)
  • Innovation gains. This category is more difficult to figure out but is worth having in the calculation. First, what would be the return on having new insights into customer/user data? (See Reason #6.) Also, what could your technical teams work on when they “get their time back” from not having to service legacy applications? (See Reason #4.)
  • Avoiding regulatory compliance costs. These could be potential fees avoided by having appropriate compliance in place, especially where data retention and data privacy are concerned.
In many cases, the direct savings and efficiency gains alone are enough to justify an application retirement solution; innovation and peace-of-mind are the cherry on top.
The case is clear: Applications do become redundant or obsolete with time. It is costly, and risky, to let them sit there on the system. Retiring them appropriately and archiving the data they contain is the only way to maintain security while keeping appropriate access.
Interested in finding out more about application retirement, and how Infobelt can help with this critical service? See what we offer.

Share News

Categories
Blogs

Why Smaller Firms Are Getting Hammered: Myths of Scale

Why Smaller Firms Are Getting Hammered: Myths of Scale

There is a bit of folklore in most heavily regulated industries, and especially in the financial services industry, that goes something like this: Larger players should worry the most about regulatory compliance and security.
The reasoning is simple: Larger, well-known firms are the ones most likely to be targeted by both cyber criminals and government agencies. Smaller firms—even moderately sized ones—will tend to “fly under the radar” of both, and so can put off investing in technology (RegTech) or training until the time when they have grown big enough to attract attention. In short, worrying about compliance or cybersecurity is a matter of scale, but only in the roughest sense: There are two size classes, bigger firms that need to worry about these things, and everyone else.
The term “folklore” is apt here because this kind of thinking never is written explicitly, but it is assumed by many. And while it might have applied in the past, it is surely not the case now—which means that too many regulated firms are toiling under a false, and risky, assumption.
With Automation, Everyone is On the Radar: The Ransomware Example

So what has changed?

Let’s look at the logic with a specific example: Ransomware gangs that try to gain a foothold in a business to take a chunk of the firm’s data hostage.
Not too long ago, gaining access to critical business systems took some time and diligence on the part of the hackers. They had to either undo the company’s security and encryption, or else dupe an employee into giving up their credentials (easier to do, the more employees there are). Because an attack took time and effort, it made sense for hackers to go “big game hunting”—that is, to try to get the best bang-for-their buck by targeting larger firms with bigger cash flows. That is where the best payoff would be.
What has changed since those days is automation. A ransomware gang can now target hundreds of firms of various sizes, all at the same time, looking for vulnerabilities and beginning spear-phishing attacks to gain system access. They can then focus their energies on those that prove vulnerable, even if the payoff is much less for any one successful attempt.
And which firms tend to be the most vulnerable? It is exactly the small-to-medium-sized firms, because they have bought into the folklore that says hackers won’t bother targeting them. Having bought into the folklore, they don’t take the necessary steps to protect themselves.
Think of it as a contrast between a cat burglar and a gang of street thieves: The cat burglar spends his time trying to pick the lock on a single door, hoping there is a stash behind it. But what the gang of thieves lack in skill and finesse, they more than make up for in manpower: They simply try every door, hoping that, eventually, one will be unlocked. The unlocked rooms might not be as lucrative, but they are also much less likely to have adequate security measures in place, too. Today’s hackers are no longer cat burglars, they are gangs looking for easy scores—and smaller firms are exactly that.
Regulatory Compliance is Playing the Same Game
Ransomware is just one example of a risk to which firms of all sizes are now exposed. A similar logic now applies to regulatory compliance, too.
Government institutions, for a long time, went after bigger firms, believing they would be the most egregious offenders when it came to compliance. Smaller firms would not attract much scrutiny, unless something was directly brought to the attention of regulators.
This is no longer the case, and again, automation is part of the story. For example, government firms are now using automation and artificial intelligence to “find the needle of market abuse in the haystack of transaction data,” using various algorithms to scrape the web for deceptive advertising and capturing red flags that might indicate wrongdoing. They are also using these tools to zero in on accounting and disclosure violations. Regulators can now spot potential problems more quickly and quietly than ever before, and now more small firms are getting MRA letters from regulators, surprised that they are no longer invisible.
This is an importantly different phenomenon from regulatory tiering. It has always been the case that many regulations carve out exceptions for smaller businesses, when strict compliance would be an undue burden on them. For example, health insurance mandates and employment laws have clauses that exclude firms of a particular size. While it can be debated how and when such tiering should occur, the fact is that many businesses fall under the more regulated tier by law, but have traditionally escaped scrutiny because they were “small enough.” Those days are now over.
Beware, Data Scales Quickly
Part of the issue for financial services firms is not only the sheer amount of data they generate, but the kinds of data they generate.
The volume of data generated correlates pretty well with the size of a firm. This makes sense: The larger the firm, the larger the customer base, and the more transactions happen every day.
But the compliance nightmare comes more from the huge variety of data generated by financial services firms, and that variety does not scale: It’s huge, whether you are a small local firm or a large international one. For example, on top of transactional data, a financial services firm might have
  • Client personal data (name, address, birthday, Social Security number, etc.)
  • Credit information
  • Mortgage and loan information
  • Contract information
  • Email and other logged communications
  • Employee personal information and pay information
  • Analytics data (based on customer spending patterns, segments, new products, customer feedback, etc.)
  • Marketing data (email open rates, website visits, direct mail sent, cost of obtaining a new customer, etc.)
…and much more. That data often resides on different servers and within an array of applications, often in different departments.
This means that, when it comes to complying with data privacy laws, or protecting data with the right cybersecurity measures, size doesn’t matter. The variety of data is a problem for firms of all sizes.
Moral of the Story: Smaller Firms Need Protection, Too. Yes, You.
The folklore says that smaller regulated firms can put off investment in cybersecurity and RegTech simply because cyber threats and regulatory scrutiny will “pass over” smaller firms and land, instead, on the bigger players.
That is no longer the case. Both cyber criminals and government regulators are using tools to spot problems more quickly and easily, and it is worth their while to set those tools to investigate everyone. (We’ll let readers decide which they would rather be spotted by first.) Indeed, small- and medium-sized firms are having a more difficult time now, because it is much less common for these firms to have proactively invested in preventive solutions.
So what do you do if you are a smaller company in a heavily regulated industry? The first step would be to look into technology that can give you the most protection for your dollar. After all, if cybercriminals and government agencies are going to use advanced digital tools, you should too. Having an immutable data archive, automated compliance workflows, and application retirement tools are all a good beginning.
The alternative would be to do nothing, and hope that your turn will not come up. But strategies based on folklore have never been very good at reducing risks—quite the contrary.

Share News

Categories
Blogs

Legacy Data is Valuable, but Legacy Applications Are a Risk. What’s a Finserv IT Department to Do?

Legacy Data is Valuable, but Legacy Applications Are a Risk.
What’s a Finserv IT Department to Do?

In January 2022, infosec and tech news sources blew up with stories about “Elephant Beetle,” a criminal organization that was siphoning millions of dollars from financial institutions and likely had been doing so for over four years.
This group is sophisticated. It is patient. But the truly frightening part of the story was that the group was not using genius-level hacking techniques on modern technology. On the contrary: Their MO is to exploit known flaws in legacy Java applications running on Linux-based machines and web servers. Doing this carefully, the group has been able to create fraudulent transactions, thereby stealing small amounts of money over long periods. Taken altogether, the group managed to siphon millions unnoticed.
What this story reveals is that legacy applications are a huge liability to financial institutions. They have known vulnerabilities that, too often, go unpatched, and they can be exploited without raising an alarm.
Even if they are not breached by bad actors, such systems can be a drag on institutional resources. According to research done by Ripple back in 2016, it was estimated that maintaining legacy systems accounted for some 78% of the IT spending of banks and similar financial institutions.
The question is: Why are financial institutions holding onto these legacy applications? And what can be done to minimize the risk they pose?
The Need to Retain Legacy Data
Most companies have at least one legacy application they still support simply because they need its data. In some cases, that data has to be held to fulfill retention requirements for operational or compliance purposes. In other cases, that data holds important business insights that can be unearthed and used profitably. And in some cases, it’s a mix of both.
But with the march of technological progress, it’s easy to see how this situation can get out of hand. Some organizations have hundreds of legacy applications they’ve accumulated over time just to keep easy access to the data stored within them. The vast majority of those applications are no longer being fed live data, having been replaced by next-generation solutions. But they sit on the organization’s network just the same, taking up resources and allowing bad actors to have a back door to other more mission-critical applications.
Because these applications are no longer in use or are being updated with new data, it does not make sense to patch and maintain them continually. (And in most cases, patching is not even an option as no one is supporting the legacy application anymore.) To really make them safe, they should instead be retired. Application retirement, done correctly, can render an application inert while still providing select users access to its data.
Application Retirement is Now a Part of Finserv IT Best Practices
Application retirement is the process used to decommission applications (and any supporting hardware or software) while securely keeping their data accessible to maintain business continuity. As applications grow, develop, and change, the number of legacy applications that need to be carefully retired grows exponentially.
An application retirement program is simply a strategy, including a schedule and a set of rules, for ensuring that application retirement happens correctly. Any application retirement program must ensure that the right data is retained and in the correct business context so that the data remains meaningful to business users (or auditors) long after the legacy application is decommissioned.
More and more businesses are looking for increased sophistication when it comes to such programs because they provide:
  • Direct savings by eliminating legacy support and maintenance costs (which, again, can account for 78% of financial institutions’ IT budget).
  • Efficiency gains by delivering effortless access to historical business data.
  • Regulatory compliance through application retention rules that manage data securely throughout its lifecycle.
That said, the most challenging part of a retirement program often is getting it off the ground. Getting leadership buy-in, determining retirement criteria, and classifying all of the applications in the ecosystem can produce hurdles to effective execution.
And even when those hurdles are cleared, the process itself takes a great degree of technical expertise to execute. System analysis; data extraction, processing, and storage; user access design—all of these need to happen seamlessly before the application itself can be successfully retired.
Getting the Expertise on Board for a Successful Application Retirement Program
We’ve only scratched the surface here regarding best practices around application retirement. Those interested in learning more about the application retirement process and the best practices around a robust application retirement program are encouraged to download our white paper: Application Retirement: The Practical Guide for Legacy Application Maintenance.
But even with this quick glimpse, two things are already clear. One is that it takes a good deal of technical know-how, as well as best-in-class tools and technology, to successfully retire applications while still making the critical data within them accessible.
The other is that relying on the status quo is simply not a viable option. While the organizational and technical hurdles to proper application retirement are substantial, their cost is far outweighed by the cost of doing nothing—which includes not only wasting valuable IT department time and budget but also leaving the back door open to bad actors.
Interested in tools that make application retirement easier? Ask us about Infobelt’s Application Retirement Workbench, available with Omni Archive Manager.

Share News

Categories
Blogs

Archiving vs. Backup: Which Do You Really Need for Compliance?

Archiving vs. Backup: Which Do You Really Need for Compliance?

Everyone who has worked in records management has seen it before: Organizations keeping their backup copies of production data “because it’s needed for compliance.” This, however, turns out to be a costly move…and one that does not really address data retention needs. What is really needed for data retention is a proper data archiving system.
Which prompts the question: What is the difference? Why is backup not suitable for compliance, and what is gained from investing in a true enterprise data archive?
Archiving vs. Backup: Two Different Missions
The short answer to the above is that archiving solutions and backup solutions were created with two different goals in mind:
  • Backup makes a copy of data (both active and inactive) so that, should that data become damaged, corrupted, or missing, it can be recovered quickly.
  • Archiving makes a copy of inactive or historical data so it can be stored in an unalterable, cost-effective way for legal or compliance reasons.
Backup is an important part of a business continuity plan. Should a piece of hardware fail, or a database become corrupted, it still will be possible to recover the necessary data to keep business operations going.
Maintaining a backup system can be costly, however. The data in the system needs to be updated often, and made easily recoverable, should a disaster happen. The space and cost required to do so can become quite large as an organization’s data grows.
Archiving stems from the realization that not all data an organization has is needed for daily operations—it is not production data. Examples include old forms, transaction records, old email communications, closed accounts, and other historical data. But while this data has no ongoing use, it has to be kept to comply with laws having to do with data retention.
It’s easy to see how the two might be confused—after all, both kinds of technology are, in essence, making a copy of the organization’s data.
But whenever you have two different goals or purposes for two different pieces of technology, you are going to have some important differences as well. If those differences are large enough, you won’t be able to simply swap one technology for the other. At least, not without some major problems.
First Major Difference: The Cost of Space
When a bit of data is stored, there is a cost associated with it. That’s true whether that data sits in the cloud, on an on-prem server, or on a tape drive in a closet somewhere.
Not all storage costs are equal. Take cloud providers like AWS, Microsoft (Azure), and Google, for example. These big players tier their storage offerings, basing the price on things like accessibility, security, and optimization for computations. “Hot storage” holds data that might be used day-to-day and needs to be optimized for computing, and so is relatively much more expensive. “Cool” or “cold” storage is for data that is rarely used, and so does not need to be optimized or accessed quickly. Thus, it tends to be cheaper—sometimes by half or more.
The same goes for on-prem storage. Some data needs to be readily accessible, and so located on a server that needs to be maintained and secured. There are many more options for data that does not need to be accessible, like historical data.
The longer an organization stays up and running, the greater its older, inactive historical data is in proportion to its active data. This is why archiving is important: It saves this inactive data in a much more cost-efficient way, freeing up the systems that traffic in active data (and freeing up storage budget).
Second Major Difference: Immutability
An important part of compliance with data retention laws is keeping the data in an unaltered, and unalterable, state. This is where the idea of immutable storage comes into play. Immutable storage, such as a WORM (write once, read many) datastore, cannot be altered, even by an administrator. The data is, in a sense, “frozen in time.”
This is important for legal purposes. If data is needed for any reason, it is important to show that it has been stored in a way that resists any sort of tampering or altering. In short, immutability is built into most data archiving solutions, because immutability is important for the very tasks for which archives were engineered. The same might not always be true for data backups.
Another benefit of immutability: It provides built-in protection against ransomware attacks.
An important part of compliance with data retention laws is keeping the data in an unaltered, and unalterable, state. This is where the idea of immutable storage comes into play. Immutable storage, such as a WORM (write once, read many) datastore, cannot be altered, even by an administrator. The data is, in a sense, “frozen in time.”
Third Major Difference: Logging and Tracking
Along with alterability comes the idea of logging or tracking who has accessed a particular bit of data. Having a log of who accessed which data, and when, leaves an important trail of breadcrumbs when it comes to audits, as well as data privacy incidents. Most backup systems do not need this level of logging and tracking—they usually carry just enough information to verify when backup or recovery has been run, and how successful it was. Archiving provides a much more granular level of detail.
Fourth Major Difference: Scheduled Destruction
Once data is no longer needed for compliance purposes, it should be destroyed. That way, it no longer takes up space, nor runs the risk of being compromised (which can be a data privacy issue).
Best-in-class archives, because they are focused on compliance needs, have such scheduled destruction built in. Backup systems usually do not, as that would be antithetical to their purpose of saving data. (At best, backup systems overwrite previous backups, and some let the user determine how many backup copies need to stay current.)
Archiving and Backup: Which Does Your Organization Need? (And How Do You Know?)
Really, most enterprise-sized organizations need both. Business continuity plans need to include solutions for backup.
But those solutions make for a very costly, and mostly inadequate, archiving solution for compliance purposes. Different technology is needed for this.
So, if your organization is discussing disaster recovery and prioritizing things like speed to get up and running again with your production data intact, it’s best to explore a backup solution.
But if, like the customers above, you are looking to retain records or other data for compliance purposes, invest in a data archive.
Barry Burke, storage guru and CTO of Dell EMC for years, has a great way of conceptualizing the difference between the two technologies, looking not at what is done, but what the intent behind the action is:
In explaining the word “archive” we came up with two separate Japanese words. One was “”katazukeru,” and the other was “shimau”…Both words mean “to put away,” but the motivation that drives this activity changes the word usage.

The first reason, katazukeru, is because the table is important; you need the table to be empty or less cluttered to use it for something else, perhaps play a card game, work on arts and crafts, or pay your bills.

The second reason, shimau, is because the plates are important; perhaps they are your best tableware, used only for holidays or special occasions, and you don’t want to risk having them broken.
If plates are data and the table is your production storage system, then backup is shimau: The data is important to save, even at a high cost. Archiving is katazukeru: It’s the system itself that must be cleared so you can get on with the next activity…but, of course, you still want and need to save the plates.
Interested in what an archiving solution can do for your organization above and beyond backup? Take a look at our Omni Archive Manager, or reach out to talk to one of our specialists.

Share posts

Request a Demo

Speak with a compliance expert today to learn how your enterprise
can benefit from Infobelt’s services.

Rijil Kannoth

Head of India Operations

Rijil is responsible for overseeing the day-to-day operations of Infobelt India Pvt. Ltd. He has been integral in growing Infobelt’s development and QA teams. Rijil brings a unique set of skills to Infobelt with his keen understanding of IT development and process improvement expertise.

Kevin Davis

Founder and Chief Delivery Officer

Kevin is a co-founder of Infobelt and leads our technology implementations. He has in-depth knowledge of regulatory compliance, servers, storage, and networks. Kevin has an extensive background in compliance solutions and risk management and is well versed in avoiding technical pitfalls for large enterprises.