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Crafting Corporate Harmony through Innovative GRC Alchemy

Infobelt

Srini Mannava

Founder & CEO

“We understand all of those, and we were able to retire a bunch of their data into the cloud platforms that were set up specifically to meet those requirements in several parts of the world, so that way the customer actually can access the data anytime they want, and they avoid paying all of this maintenance.”
In an era where the corporate landscape is continually evolving, organizations grapple with a myriad of challenges, from data dispersion and security breaches to the complexities of regulatory compliance. Navigating these turbulent waters requires more than just technological solutions; it demands a profound understanding of the intricate nuances of corporate governance. Infobelt, a trailblazing force that emerges not merely as a solution provider but as a transformative ally in the realm of Governance, Risk, and Compliance (GRC).
Infobelt is a source of innovation and knowledge for businesses struggling with the ever-increasing challenges of data management, security, and regulatory compliance. Infobelt was founded by a group of founders with decades of combined experience in the corporate sector, thus the company has a firsthand understanding of the difficulties that businesses encounter. This knowledge serves as the cornerstone of Infobelt’s distinctive value proposition, which combines technology expertise with client-speaking skills.
Infobelt goes beyond what is often expected of a supplier of GRC solutions. It becomes a strategic partner, providing advanced technology together with a helping hand to navigate the maze of client demands. Infobelt is leading the way in introducing a new era of comprehensive and integrated GRC solutions, whether it is by solving coordination issues in large businesses or customizing solutions for smaller clients with particular demands.
The main advantage of Infobelt is that its founders and employees speak the same language as its clients because they have lived through and experienced the difficulties of working in the corporate environment. Infobelt is able to close the gap between technical goods and practical client solutions because of this unique edge. The group can interact with customers at several organizational levels, such as CFOs, CDOs, and CIOs, providing them with a thorough grasp of their requirements.

“We understand all of those, and we were able to retire a bunch of their data into the cloud platforms that were set up specifically to meet those requirements in several parts of the world, so that way the customer actually can access the data anytime they want, and they avoid paying all this maintenance,”

says Srini Mannava, Founder & CEO of Infobelt.

Infobelt helps clients of all sizes with problems they encounter in a variety of sectors. Infobelt excels in offering solutions for information, data security, and governance concerns, whether serving smaller businesses with particular difficulties or enterprise-sized organizations with substantial income and assets. The platform easily interfaces with on-premises systems, cloud-based solutions that are constantly growing, and internal applications.
The method used by the business to solve problems varies in size. Coordination and communication between departments can be difficult for larger businesses. Infobelt provides a uniform platform to help with this. Smaller and middle-tier clients gain from customized solutions including data transfer, isolation, and security that meet their unique requirements.
The core components of Infobelt’s flagship product are data protection and long-term records management. The platform offers complete records management, governance, and safe data storage. It is made to handle a variety of data formats. Infobelt guarantees scalable solutions by utilizing robotic process automation and machine intelligence, as evidenced by its accomplishments with a top-five global financial institution.
A notable financial institution that struggled with departmental disparities, data dispersion, and regulatory compliance is the subject of one noteworthy case study. More than 400 apps were successfully onboarded as a result of the data management process being expedited using Infobelt’s platform. The customer avoided risks related to data security and compliance and passed stringent regulatory examinations thanks to its secure storage of 30 trillion records.
In another scenario, a worldwide commercial real estate management company was having trouble with antiquated technologies and older ERP platforms. Infobelt assisted in the data reporting and archiving, modernizing their data management procedures, and enabling cost savings and regulatory compliance.
A culture of people-centeredness is fostered at Infobelt, with a focus on a mutual commitment to client success and a positive work-life balance. The organization promotes creativity and encourages team members to offer ideas, creating a climate in which everyone’s opinion is appreciated regardless of position. Infobelt’s commitment to leading the way in technology is demonstrated by its investments in artificial intelligence and machine learning, which foster a dynamic and innovative work environment.
The foundation of Infobelt’s success is its capacity to offer comprehensive solutions that fully address the problems faced by clients. Infobelt is shaping the future of GRC solutions with a committed workforce, a forward-thinking strategy, and a focus on client happiness.

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Embracing the Future: Digital Transformation in Modernizing IT Infrastructure

Infobelt

Introduction
Digital transformation might seem like a new concept, but the reality is that it has a long history. In the 1970s, computer-aided manufacturing and design were first used in business. We saw the introduction of enterprise resource planning in the 1980’s. eCommerce and online banking were introduced in the late 1990s, and social media emerged in the mid-2000s.
Companies first started using these digital channels as a way of connecting with and supporting their customers.
We have seen a renewed focus on digital transformation over recent years as work has become digital, mobile, and social. Companies striving to remain competitive globally have prioritized digital transformation in 2024. Below, we will talk about where these companies are starting.
Understanding Digital Transformation
Digital transformation is a holistic approach to altering business processes, culture, and customer experiences to meet changing market and business requirements. This strategic initiative integrates digital technology into all business areas, fundamentally changing how businesses operate and deliver customer value. These changes will drastically improve the experience for both the employee and the customer.

Key Components of Digital Transformation:

  1. Cloud Integrations: Moving towards a hybrid or multi-cloud environment to optimize performance, cost-effectiveness, and flexibility for remote work. 
  2. Cybersecurity: Cybersecurity remains a top priority with the continued expansion of digital infrastructures. This involves comprehensive strategies encompassing firewall protection, intrusion detection, data encryption, and secure network segmentation. The proactive approach to cybersecurity is crucial for safeguarding organizations. 
  3. IT Modernization: IT modernization encompasses various strategies and practices to upgrade and optimize an organization’s IT infrastructure, systems, and business applications to meet the current security and technology needs. 
  4. Data-Driven Decision Making: Leveraging data analytics for better decision-making and strategic planning.
  5. Sustainability: Sustainability has become a guiding principle in business decision-making. Companies are looking for ways to reduce their carbon footprint and operate more sustainably, leveraging digital technologies for energy efficiency and reduced paper use. 
  6. Total Experience (TX): Focusing on enhancing customer and employee experiences, integrating technologies to create superior shared experiences. 
IT Modernization: A Pillar of Digital Transformation
IT modernization is crucial in digital transformation, upgrading existing technology systems and infrastructure to improve efficiency, security, and scalability. It’s about moving from outdated processes and legacy systems to more agile, cloud-based solutions.
  1. Application Modernization: Improving and upgrading existing software applications to align with the latest technology to meet customer demands. It’s about decommissioning or retiring legacy applications and adopting more modern, cloud-based, data-driven platforms created with coordination and scalability in mind. 
  2. Infrastructure Modernization: Upgrading the hardware, servers, and networking components that support a company’s IT operations is crucial. This can include adopting converged or hyper-converged infrastructure systems, making the infrastructure more manageable, scalable, and easier to repair. 
  3. Data Modernization: This aspect focuses on organizing and transforming data into a more usable and valuable format. It typically involves consolidating data into one unified platform and analyzing data to gain valuable insights, which can guide business decisions and strategies. 
  4. Lifecycle Modernization: Reshaping internal operational processes, including managing IT hardware and devices throughout their lifecycle, is essential to IT modernization. 

Benefits of IT Modernization:

  1. Enhanced Efficiency: Streamlined operations with faster, more reliable systems.
  2. Improved Security: Advanced security measures to protect data and assets.
  3. Scalability: Easier adaptation to market changes and business growth.
  4. Cost Reduction: Lower operational costs by eliminating legacy system maintenance.
Legacy Application Retirement: Clearing the Path for Innovation
Legacy application retirement is an essential step in the digital transformation journey. These outdated systems often pose risks and hinder innovation due to their inflexibility and high maintenance costs.

Strategies for Legacy Application Retirement:

  1. Application Assessment: Evaluating the usefulness and efficiency of existing applications.
  2. Data Migration: Safely transferring data from old systems to a modern platform like Infobelt’s Omni Archive Manager. 
  3. Phased Approach: Gradually retiring applications to minimize disruption.
  4. Employee Training: Ensuring staff are equipped to use new systems effectively.
Aligning Digital Transformation with Business Goals
The ultimate aim of digital transformation is to align IT infrastructure with business goals. This alignment requires a strategic approach, focusing on data management, customer engagement, and operational efficiency.

Steps to Align Transformation with Business Objectives:

  1. Define Clear Objectives: Understand what the business aims to achieve with digital transformation.
  2. Involve Stakeholders: Ensure all company parts are engaged in the transformation process.
  3. Focus on Customer Needs: Use digital tools to improve customer satisfaction and engagement.
  4. Embrace Data Analytics: Utilize data for insights and informed decision-making.
Data Management in the Era of Digital Transformation
Effective data management is the backbone of digital transformation. Modern businesses are inundated with data, and managing this data effectively is critical for success.

Critical Aspects of Data Management:

  1. Data Storage and Accessibility: Implementing cloud storage solutions for better data accessibility and management. Modern platforms like Infobelt Omni Archive Manager can securely retain, index, and manage every type of data in one platform, making this easy for storage and accessing your business’s most valuable data.
  2. Data Security: Ensuring robust security protocols to protect sensitive information.
  3. Data Analytics: Utilizing advanced analytics tools to gain insights and drive business strategy.
  4. Compliance: Adhering to data protection regulations and standards.
Conclusion
Digital transformation, IT modernization, and legacy application retirement are essential for businesses looking to thrive in the digital era. By embracing these changes, companies can improve efficiency, enhance customer experiences, and make better, data-driven decisions. The journey might seem daunting, but a clear strategy and commitment lead to a more agile, innovative, and successful business model.
By: Dusty Gilvin, COO & CRO, Infobelt

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Is Your Data Safe in a Post-Quantum World?

Infobelt

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.

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

Infobelt

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.

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Navigating Legacy Application Management: Challenges and Solutions Introduction

Infobelt

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.

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

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

Infobelt

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.
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Archiving Your Way to Better Data Management and Security

Infobelt

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.

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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.

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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.

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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.

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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.