![]() Data is the lifeblood of organizations, enabling their operations and decision-making. In evolving workplaces where teams are distributed and working digitally, growing threats are posing risks to data confidentiality. Organizations need to adopt robust security practices to negate the impact on data privacy and ensure operational security. Data is at the center of every business operation and one of the most valuable assets to be protected from malicious activities that can impact organizations with increasing regulatory fines, reduced client trust, and lost revenue. A 2024 security report by Proofpoint reveals a single data loss instance can impact 56.6% of business revenue. In evolved workplaces where growing distributed and digitized work has also given rise to threat incidents, the absence of a robust security system can cause organizations the risk of unauthorized data exposure and unrecoverable loss of critical business information. What causes data loss Not aligning with security best practices is the key reason for data loss. A security report by Proofpoint finds that careless users (70.6%) cause the most data loss incidents. The same report adds that compromised systems (48.1%) are the second most data loss threat to businesses. What is the difference between data loss and data breach The two terms are often used interchangeably but they refer to distinct phenomena as data loss happens when systems or devices in which it is stored get corrupted, damaged, or lost, while data breach involves unauthorized access to confidential business information. Forms of data loss Data loss can vary based on the instances and happen involving different scenarios, including: Device-caused data loss Data loss resulted from some activity that happened on a device or endpoint (including, a laptop, tablet, mobile phone, server, IoT devices, etc.) connected to the network. In the unfortunate event of device loss, damage, or theft, the data stored in the endpoint or device may become inaccessible. Network-caused data loss Loss of data due to an information breach from outside the organization refers to a network-caused information leak. Organizations that adopt effective security and compliance solutions can gain the advanced capability to reverse data loss instances and avoid impact on critical business information. What are the factors that can lead to data loss Causes for loss of data can vary from negligent or accidental insiders to intentional or malicious actions and non-returned or lost business devices. Negligent or intentional insiders Careless insiders deleting important files, downloading malicious apps, or clicking on a spurious link can lead to potential harm to the data privacy of concerned organizations and disrupt their business operations. Credential theft Malicious actors may take advantage of their authority for personal or financial benefits to help some third party gain access to vital internal business resources and cause sensitive data exposure. Non-returned assets Unfortunate instances of lost or stolen business devices hamper an organization’s ability to gain access to the stored details bound to its clients, projects, or users in non-returned official devices. How to control data loss Revisit your security posture and fix the potential gaps to strengthen your immunity against fatal data loss threats. Follow the best security practices Ensure data backups regularly and store them in a secure place with zero trust authentication and secure access to authorized personnel only. Adopt advanced monitoring solutions Find an effective employee monitoring system to track workforce activities and stay alert of risky actions leading to data or device threats. Ensure compliance and data privacy Ensure compliance with leading security standards to help align your workforce with data privacy best practices. Need for wAnywhere to predict and prevent data loss You can leverage advanced compliance solutions with AI security from leading employee monitoring software for real-time tracking and automated violation detection to predict and prevent actions leading to data privacy threats. AI-powered security and compliance Effective data loss prevention solutions with predictive and behavioral analytics can help you respond ahead of threats and enable compliance with established industry regulations including GDPR, HIPAA, SOC 2 Type 2, PCI DSS, ISO 27001, and more. Real-time activity monitoring With live activity tracking, you can gain the capability to respond in real time and stop threats before they can cause damage. It enables you to prevent potential impact on data privacy and negates possible disruption to business operations. Automated response and activity blocking Set automated detections against risks, including an unknown person at the desk/multiple persons around the desk/employee leaving the machine unattended/mobile use at work, and define self-triggered response, including screen black/block/OTP lock, against non-compliant activities to enhance workplace security and operational efficiency. Control anomalous behavior and prevent risks to your business data With ever-present threats of data loss and growing instances of information breaches, now is high time to stay alert against malicious actors and activities to block the actions leading to business data or device theft. Leverage AI security solutions to ensure compliance and data privacy. Start your wAnywhere experience today to know what security advantages you can gain with the trusted employee monitoring software by the leading BFSIs (Banking, Financial Services, and Insurance) worldwide. Originally Published: https://wanywhere.com/en/impact-of-data-loss-common-causes-and-effective-solutions-to-safeguard-your-business/
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How Can AI-Based Security and Compliance Safeguard Sensitive Data in Highly Regulated Industries4/4/2024 ![]() Discover how AI-based security innovations protect sensitive data in strictly regulated industries, like Healthcare, BPOs, Banking and Finance, safeguarding against data breaches and ensuring compliance governance with insider risk management. In today’s digital age, data breaches pose a significant threat to organizations, particularly those operating in highly regulated industries such as financial services, healthcare companies, and BPO businesses. These sectors handle vast amounts of sensitive data, including financial records and personal health information. The consequences of a data breach in such industries can be severe, ranging from financial losses and reputational damage to regulatory fines and legal liabilities. To address these challenges, organizations are increasingly turning to AI-based security and compliance solutions to ensure data protection policies and enhance enterprise security compliance. Common Factors for Data Breaches in Highly Regulated Industries The impact of data breaches in highly regulated industries can be significant. Such breach of data privacy can lead to severe impacts including financial losses, damage to reputation, legal liabilities, regulatory fines, and loss of trust from customers and stakeholders. Additionally, breaches in highly regulated industries may also result in breaching industry standards and data privacy compliance, further adding to the adverse consequences. Overall, the impact can be far-reaching and detrimental to the affected organizations and individuals involved. Employee Negligence Despite rigorous training and policies, employee negligence-caused errors remain one of the leading factors for data privacy breaches. Employees may inadvertently mishandle sensitive data, fall victim to phishing attacks, or fail to follow security protocols, resulting in unauthorized access, disclosure of information, and non-compliance fines. Insider Threats Insiders, including employees or contract teams, can pose a significant risk to privacy and data protection. Malicious insiders may intentionally steal or leak sensitive data for privacy breaches and personal gain, while negligent insiders may inadvertently expose data through careless actions. Compliance Failures Non-compliance with industry regulations and data protection laws, including GDPR, HIPAA, ISO 27001, SOC 2 Type 2, and more, is a significant risk factor for data breaches in highly regulated industries. Failure to implement adequate risk management solutions, conduct regular compliance audits, or report data breaches promptly can result in regulatory fines, penalties, or legal consequences. How AI-Based Security and Compliance Control Data Breaches AI-based security and compliance measures can play a crucial role in risk identification and mitigating data breaches in BPOs, along with highly regulated BFSI industries. Here’s how: Advanced Threat Detection AI-powered security solutions leverage machine learning algorithms to detect and respond to emerging threats in real time. These solutions analyze vast amounts of data from multiple sources, including network traffic, user behavior analytics, and system logs, to identify anomaly detection indicative of potential security breaches. Behavioral Analytics AI-based security platforms utilize behavioral analytics to establish baseline behavior for users, devices, and applications. By monitoring deviations from normal behavior patterns, these solutions can detect insider threats, account compromise, or unusual activity that may indicate a risk to data compliance. Predictive Analytics AI-driven predictive analytics enable organizations to anticipate and proactively mitigate security risks before they escalate into full-fledged data breaches. By analyzing historical data, threat intelligence feeds, and contextual information, AI security measures can identify vulnerabilities, prioritize remediation efforts, and prevent future attacks. Automated Compliance Management AI-based compliance solutions streamline regulatory compliance efforts by automating compliance assessments, audits, and reporting processes. These solutions help organizations enhance compliance governance to industry regulations, data protection laws, and internal policies, reducing the risk of non-compliance-related data breaches. Incident Response Automation AI-driven incident response platforms automate various aspects of incident detection, analysis, and remediation. These platforms leverage orchestration and automation capabilities to streamline incident response workflows, minimize response times, and contain security incidents effectively for risk mitigation and data exfiltration prevention. Real-Time Monitoring AI-based security systems can provide continuous compliance monitoring of the workforce, systems, and workplaces adjusting security protocols in real-time based on evolving threats and vulnerabilities. Conclusion AI-based security and compliance solutions play a critical part in controlling data breaches and enhancing cybersecurity resilience in industries with data-intensive business processes. By leveraging advanced AI technologies such as machine learning, behavioral analytics, and predictive analytics, organizations can detect and respond to threats more effectively, ensure regulatory compliance, and safeguard sensitive data from unauthorized access or disclosure. As the threat landscape continues to evolve, AI-based security solutions will remain indispensable tools for protecting critical assets and maintaining trust in compliance-bound industries. Start a free trial today to get first-hand experience of how real-time monitoring is enabling data-critical industries, like yours, to ensure governance risk and compliance for operational security and efficiency. Originally Published: https://medium.com/@wanywheresolution/safeguarding-sensitive-data-ai-based-security-and-compliance-in-regulated-industries-eab25d2b17f1 The role that data analytics plays in our world is massive, it has shifted the way information can be processed. In many ways, data analytics has become crucial to almost every sector as most industries are run by processing data. The benefits of data analytics have allowed businesses to grow and flourish by mitigating risks, enhancing efficiency, and enabling strategic decision-making. The age of technology meant that there was a need for comprehensive tools and practices that could help to disseminate data. When mountains of data began collecting every day, the need of the hour was to find a way to analyze and comprehend this data. In a data-centric world, the need for data analytics is more pertinent than ever before. In this blog, we will be looking into how data analytics helps businesses operate, especially for business process outsourcing. To understand how data analytics can resolve process-related issues in business process outsourcing, we first need to understand why data analysis is important for this industry. Data analytics has the power to transform the way that business process outsourcing companies operate. By analyzing large amounts of data, BPOs can identify employee productivity at work and process-related issues that may be hindering workforce efficiency and effectiveness. Data analytics tools and techniques help BPOs uncover hidden patterns and trends, identify inefficiencies, and gain insights into the root causes of problems. With this knowledge, BPOs can then develop strategies to address these issues and improve their operations. Benefits of Data Analytics: 4 Factors Addressing Process Issues in BPO Data analytics has the potential to significantly improve the operations of BPOs by helping them streamline processes, identify new growth opportunities, improve customer satisfaction, and identify and address issues that may be affecting the quality of their services. By implementing a data analytics platform or working with a data analytics service provider, BPOs can maximize the benefits of data analytics and drive business success. 1. Streamlines Processes By analyzing data on how processes are currently being carried out, BPOs can identify bottlenecks, waste, and other inefficiencies. With this information, they can then redesign processes to eliminate waste and improve employee efficiency. For example, call center data analytics might reveal that a particular process is taking longer than necessary due to unnecessary steps or a lack of coordination between departments. By addressing these issues, BPOs can reduce the time and resources needed to complete the process, leading to cost savings and improved productivity. 2. Identifies Growth Opportunities BPOs can identify areas where they can expand their services or enter new markets by analyzing data on customer behavior, market trends, and other factors. For example, data analytics might reveal that a particular product or service is in high demand in a particular region, providing an opportunity for the BPO to focus on serving that market. By staying ahead of trends and identifying new opportunities, BPOs can stay competitive and continue to grow their business. 3. Improves Customer Satisfaction Improving customer satisfaction is another area where data analytics can be particularly useful for BPOs. By analyzing data on customer interactions, BPOs can identify areas where they can improve their services. For example, data analytics might reveal that customers are frequently experiencing long wait times when trying to contact customer service. By addressing this issue to optimize employee time at work, BPOs can improve the customer experience and increase satisfaction. Additionally, data analytics can help BPOs identify which products and services are most popular with customers, enabling them to focus their efforts on meeting the needs of their most valuable customers. 4. Recognizes Process Issues Data analytics can also be used to identify issues that may be affecting the quality of a BPO's services. By analyzing data on the performance of various processes and systems, BPOs can identify problems that may be leading to errors or other issues. For example, data analytics might reveal that a particular process is prone to errors due to a lack of standardization or inadequate training. By addressing these issues, BPOs can improve the quality of their services and reduce the risk of errors. Future of Data Analytics Data analytics is a constantly evolving field that has revolutionized many industries by helping businesses ensure security and compliance, increase work efficiency, and optimize employee productive hours. The potential applications of data analytics in the future are numerous and varied, making it an invaluable tool for strategic decision-making and planning.
One of the major advantages of data analytics is its speed and accuracy in analyzing large volumes of data. Tasks that would have taken people hours, days, or even weeks to complete can now be done in a matter of minutes, thanks to the computational nature of data analytics. This eliminates the possibility of human error, which can be a significant benefit in today's data-dense society. As the use of data analytics continues to grow, it will be a key tool of the future. Its limitless potential across every sector makes it an essential tool for businesses looking to stay competitive and succeed in today's fast-paced, data-driven world. ![]() Distributed work can easily get impacted by distractions and gaining timely information about when teams do what activities help measure how productive they perform — each employee hour being wasted results in growing inefficiency at work. If you don’t have a productivity monitoring system, you’re risking your workforce to end up with teams other than those that makeup 75% of employees that reflect maximum productivity in remote work. Gaining awareness about how teams work in distributed environments helps identify what teams perform and what teams lack in doing productive work. Here the employee data entailing employee performance factors can prove useful for organizations struggling to know what drives or disrupts employee productivity in remote work. Let’s understand how employee productivity monitoring software helps capture employee data based on their activities and what insights you can gain by processing this data into actionable information: Get employee activity-based data Find out how employees perform and what causes inefficiencies at work. Measure employee performance with an effective monitoring tool to learn how employee activities impact productivity at work. What are workplace distractions Employee workplace data includes what their active time is, and what idle or break time they spend. You get to know what aux time BPO teams use. This data is valuable and holds the key to finding out how employees spend the day and what their work hours are. You can get this data analyzed with dashboard visualization from remote employee productivity monitoring software to get your answer on how your employees spend the day at work. What is employee engagement at work Employee engagement data from employee monitoring software reflects what employees deliver quality work and what employees lack in meeting expectations for standardized work. Process workforce data gained with the remote employee productivity monitoring tool to analyze how your teams perform and with what productivity. What apps and websites help employees perform Capture app and web usage-based employee data with staff productivity monitoring software that hints at what productive and non-productive time they spend at work. Get data-driven insight with employee performance monitoring software to find how employee app and web usage impact their work hours and whether they contribute to employee efficiency at work. What are employee work patterns Employee work-bound data recorded with employee performance tracking tool indicates how your teams spend their day. Measure employee data recorded with employee performance tracking software to gain real-time analytics into when employees do what tasks and whether it accelerates or distracts their productivity at work. Find actionable information about employee productivity Gain workable insights from employee data recorded with remote employee productivity monitoring tool and evaluate what factors encourage or disrupt employee performance. Identify employee work hours Measure employee time to extract the details about when employees log into work, if employees exceed idle time that impacts their active hours, or if employees use frequent break time that affects their efficiency. Analyze when your BPO teams use aux time for what duration and if it disrupts employee workflow. Detect employee utilization Employee work data also helps you identify if employees are overworking or underutilized with bandwidth to balance work. Evaluate how employees are engaged at work and what productive hours they reflect. By analyzing employee work data, you can detect factors that impact employee work efficiency. Evaluate how focused employees perform With employee performance data, you can process it to measure if employees deliver standardized work, or if their work invites multiple reviews to meet the work expectations. Find out if employees consume more time to complete the tasks and assess training opportunities for them. Get to know when to check in to support employees to boost focused time at work and help elevate their performance. Measure employee activity timeline Gaining insights from employee productivity data, you can know what activities they perform at what time and if it pushes or pauses their efficiency. Utilize employee data to break down their activities and identify when they do what actions and how it affects productivity at work. Need for wAnywhere employee productivity monitoring software wAnywhere productivity monitoring software is purpose-built for remote and hybrid teams. Organizations with distributed workforce can capture the needed employee data with AI-powered employee productivity management tool that enables them to control the factors improving or impacting employee productivity at work. Start a free trial to see wAnywhere in action today! Originally Published: Importance of Employee Productivity Monitoring in Distributed Work |
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