Tag: AItech Article

Artificial intelligence (AI) is a relatively new field that has rapidly evolved into a major influence on the strategic direction of organizations. Its significance extends far beyond automation, enhancing complex decision-making processes. AI is both a risk and a tool for managing risk—a paradox that organizations must confront as they navigate the landscape of 2024 and beyond.

AI as a Catalyst for Transformation

While AI is often associated with task automation, it also plays a critical role in improving decision-making. AI empowers change across various domains, from social to informational, by automating time-consuming processes and driving efficiency. Additionally, AI offers deeper insights to management teams than ever before.

In finance, for example, AI models outperform traditional methods by evaluating a broader set of factors to assess credit risk, predict market trends, detect fraud, and identify optimal investments. Similarly, in healthcare, AI enables early diagnosis and increases diagnostic accuracy, transforming how medical treatments are managed. These examples demonstrate that AI not only mitigates risks but also reshapes operational behavior, opening new avenues for efficiency and effectiveness.

Machine Learning’s Role in Enterprise Risk Management

Machine learning—one of the most crucial AI fields—plays a vital role in Enterprise Risk Management (ERM). By learning from data and detecting patterns beyond human observation, machine learning is particularly useful in industries like cybersecurity, where threats are constantly evolving. AI systems also monitor network activities in real-time, providing alerts for suspicious events to prevent breaches.

According to Gartner’s 2024 report, companies leveraging AI-based risk management tools saw a 30% reduction in data breach incidents. This statistic emphasizes AI’s ability to prevent risk events. Moreover, as data protection laws become stricter, AI helps organizations maintain compliance through precise monitoring and reporting mechanisms.

The dual role of AI as both a risk and a risk management tool defines the modern business landscape. Organizations that recognize AI’s strategic value and incorporate it into their planning will be well-positioned to thrive. The improvements in decision-making, efficiency, and risk forecasting that AI offers are too significant to ignore.

However, these opportunities come with responsibility. Companies must adopt ethical AI practices and ensure robust data protection to avoid negative societal impacts. Failure to address these issues could have serious consequences, not only for businesses but also for society as a whole.

Ultimately, the question is not whether to adopt AI but how to implement it sustainably and responsibly. Leaders with a vision for ethical AI usage will not only mitigate risks but also unlock new opportunities previously beyond reach. As business environments continue to evolve rapidly in 2024 and beyond, organizations that fail to adapt will fall behind. Integrating AI as both a tool and a mandate is essential for any innovative organization looking to succeed.

To Know More, Read Full Article @ https://ai-techpark.com/ai-is-both-a-risk-and-a-tool/

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Generative AI – a technology wonder of modern times – has revolutionized our ability to create and innovate. It also promises to have a profound impact on every facet of our lives. Beyond the seemingly magical powers of ChatGPT, Bard, MidJourney, and others, the emergence of what’s known as RAG (Retrieval Augmented Generation) has opened the possibility of augmenting Large Language Models (LLMs) with domain-specific enterprise data and knowledge.

RAG and its many variants have emerged as a pivotal technique in the realm of applied generative AI, improving LLM reliability and trustworthiness. Most recently, a technique known as Graph RAG has been getting a lot of attention, as it allows generative AI models to be combined with knowledge graphs to provide context for more accurate outputs. But what are its components and can it live up to the hype?

What is Graph RAG and What’s All the Fuss About?

According to Gartner, Graph RAG is a technique to improve the accuracy, reliability and explainability of retrieval-augmented generation (RAG) systems. The approach uses knowledge graphs (KGs) to improve the recall and precision of retrieval, either directly by pulling facts from a KG or indirectly by optimizing other retrieval methods. The added context refines the search space of results, eliminating irrelevant information.

Graph RAG enhances traditional RAG by integrating KGs to retrieve information and, using ontologies and taxonomies, builds context around entities involved in the user query. This approach leverages the structured nature of graphs to organize data as nodes and relationships, enabling efficient and accurate retrieval of relevant information to LLMs for generating responses.

KGs, which are a collection of interlinked descriptions of concepts, entities, relationships, and events, put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics and sharing. Here, they act as the source of structured, domain-specific context and information, enabling a nuanced understanding and retrieval of interconnected, heterogeneous information. This enhances the context and depth of the retrieved information, which results in accurate and relevant responses to user queries. This is especially true for complex domain-specific topics that require a deeper, holistic understanding of summarized semantic concepts over large data collections.

To Know More, Read Full Article @ https://ai-techpark.com/graph-rags-precision-advantage/

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Data breaches and cyber threats are becoming increasingly common in this digital era, and protecting valuable information is the top priority for data-driven organizations. To curb the constant issues of data being compromised, lost, and misused, a Data Protection Officer (DPO) and their teams can implement a data loss prevention (DLP) strategy and tools that will continuously monitor and analyze data to identify potential violations of security policies and stop them from evolving.

In this article, we will take a closer look at the seven steps of DLP strategies and tools that will help in enhancing the security of your IT structures.

Seven-Step Framework in Deploying DLP Strategy

If any business is handling sensitive data and operating in a regulated environment or suffers from repeated cybersecurity threats, it’s time that needs to add DLP strategies.

Proofpoint’s 2024 data loss landscape report indicates that 84.7% of enterprises have encountered data loss, with an average of 15 incidents per organization per year. This implies the importance of appropriately implementing DLP strategies.

Therefore, without any further ado, let’s understand the seven-step strategic framework of DLP:

Identify and Classify Your Data

To protect data effectively, DPOs need to know the exact type of data that they need to work on.

With the help of data discovery tools such as Informatica, Spectral, and Osano, data discovery administrators will scan the data repositories and report on findings, providing visibility of what needs to be protected. These tools further use regular expressions for their searches; they are very flexible but can be complicated to fine-tune.

After implementing data discovery, data administrators can use data classification software such as Varonis, Fortra Digital Guardian, and Imperva which will help them control users’ data access and avoid storing sensitive data in any unsure locations, reducing the risk of data leaks and data loss.

Use Data Encryption

In the data-centric world, encryption provides a two-step security measure that involves converting data into code that is only deciphered with a decryption key.

Organizations that deal with extremely sensitive forms of data are required to follow data security standards and regulations, including the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR). If an organization fails to comply with encrypting sensitive data, it can result in regulatory non-compliance and can lead to costly data breaches and legal penalties.

Therefore, to safeguard, data professionals can use different data encryption tools such as IBM Security Guardium Encryption, Thales CipherTrust, and Sophos SafeGuard Encryption, which add complex mathematical algorithms to data and transform it into a random series of characters that are indecipherable without the suitable decryption key.

To Know More, Read Full Article @ https://ai-techpark.com/data-loss-prevention-techniques-of-2024/

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Data has always been the backbone of business operations, highlighting the significance of data and analytics as essential business functions. However, a lack of strategic decision-making often hampers these functions. This challenge has paved the way for new technologies like data fabric and data mesh, which enhance data reuse, streamline integration services, and optimize data pipelines. These innovations allow businesses to deliver integrated data more efficiently.

Data fabric can further combine with data management, integration, and core services across multiple technologies and deployments.

This article explores the importance of data fabric architecture in today’s business landscape and outlines key principles that data and analytics (D&A) leaders need to consider when building modern data management practices.

The Evolution of Modern Data Fabric Architecture

With increasing complexities in data ecosystems, agile data management has become a top priority for IT organizations. D&A leaders must shift from traditional data management methods toward AI-powered data integration solutions to minimize human errors and reduce costs.

Data fabric is not merely a blend of old and new technologies; it is a forward-thinking design framework aimed at alleviating human workloads. Emerging technologies such as machine learning (ML), semantic knowledge graphs, deep learning, and metadata management empower D&A leaders to automate repetitive tasks and develop optimized data management systems.

Data fabric offers an agile, unified solution with a metadata-driven architecture that enhances access, integration, and transformation across diverse data sources. It empowers D&A leaders to respond rapidly to business demands while fostering collaboration, data governance, and privacy.

By providing a consistent view of data, a well-designed data fabric improves workflows, centralizes data ecosystems, and promotes data-driven decision-making. This streamlined approach ensures that data engineers and IT professionals can work more efficiently, making the organization’s systems more cohesive and effective.

Know More, Read Full Article @ https://ai-techpark.com/data-management-with-data-fabric-architecture/

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The rise of artificial intelligence (AI) has transformed many sectors across the business landscape, reshaping how organizations operate. However, the convenience of AI introduces environmental challenges, such as increased energy consumption and hardware waste. These unintended consequences call for thoughtful strategies from chief information officers (CIOs), who must balance technological advancements with sustainability goals.

According to a Gartner survey, environmental issues are now a top priority for tech companies, and CIOs are facing pressure from executives, stakeholders, and regulators to implement sustainability initiatives. The convergence of AI and environmental responsibility requires proactive measures that can drive sustainable transformation.

This article offers a framework for adopting green algorithms—energy-efficient AI solutions—to help CIOs build sustainable IT organizations.

AI Supporting Environmental Sustainability

To integrate sustainability into IT operations, CIOs must establish clear mandates and requirements to track sustainability metrics, such as energy consumption and carbon footprint. The effectiveness of these efforts depends on embedding sustainability KPIs into the organization's digital infrastructure.

A practical example lies in modern data centers. Advanced optical networks, which use fiber cables, are significantly more energy-efficient than copper-based networks. Fiber cables require fewer raw materials and less manufacturing energy, reducing the environmental impact compared to the extraction and refinement processes for copper. In fact, IT companies implementing modern networking technologies have reported up to fourfold reductions in their environmental footprint.

While AI can introduce environmental challenges, it holds tremendous potential to advance sustainability initiatives when used thoughtfully. CIOs and project managers play a pivotal role in designing and implementing AI-driven solutions that align with sustainable development goals.

By focusing AI efforts on the right use cases, businesses can mitigate environmental impacts, enhance operational efficiency, and reduce unnecessary costs. AI has the potential to become a powerful ally, fostering both innovation and environmental responsibility in the IT industry. With proactive strategies, the convergence of AI and sustainability can transform the future of business.

Know More, Read Full Article @ https://ai-techpark.com/the-convergence-of-ai-and-sustainability-in-the-it-industry/

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As the world navigates through 2024, cybersecurity gets more unpredictable and dangerous. With increased sophisticated cyberattacks like ransomware, phishing, and APTs, there has never been a higher demand for cybersecurity professionals. But after the rising tide, the industry stands at a significant skills gap, presenting organizations with vulnerabilities to breaches and data theft.

Following a report by ISC², there is a lack of more than 3.4 million cybersecurity workers across the world. The gap becomes an important threat to diverse organizations, majorly finance, healthcare, and technology-related businesses that are more vulnerable to cyber attacks. As we head to the future, B2B businesses need to invest in the next generation of cybersecurity talent and equip them with crucial knowledge and skills to offer sensitive data and systems protection.

This is your roadmap for the business to educate professionals in cybersecurity across all types of skills with education and strategies to create the best plan of workforce development.

The Growing Need for Cybersecurity Talent in 2024 and Beyond

Current Workforce Shortage

Business operations in almost every corner of the globe are being adversely affected by a severe challenge: an acute shortage of cybersecurity professionals. According to Cybersecurity Ventures, by 2025, cybercrime will reach $10.5 trillion in annual damages worldwide, representing an even greater need for experts in that field. There are not enough trained professionals to fill such a high demand. This presents a challenge to businesses, particularly those sectors dealing with sensitive data, such as healthcare, government, and finance.

Evolving Threats

The 2024 threat landscape is not only about malware and phishing schemes. Organizations are becoming increasingly subjected to more advanced attacks, like ransomware-as-a-service (RaaS), attacks by nation-state entities, and supply chain attacks—all of which make much greater technical capability demands than traditional IT knowledge requires. It is thus crucial to train professionals who can predict the magnitude of new threats.

The Role of New Technologies

Emerging technologies such as artificial intelligence (AI), machine learning (ML), and cloud computing are impacting the cybersecurity sector with new vulnerabilities; therefore, professionals have to become adept at securing these systems; thus, the urgency for cybersecurity knowledge coupled with cutting-edge tech becomes highly integral in the future workforce.

To Know More, Read Full Article @ https://ai-techpark.com/preparing-the-next-generation-of-cybersecurity-professionals/

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Have you encountered a bad situation that was made worse by something that is meant to help? Here’s a recent example of mine – I had to take my son to an emergency room while vacationing in Asia but the most frustrating part was dealing with insurance when we got home. The agent who initially processed my claim put me (and my money) in limbo – no external or internal follow-up communication, inaccessible and invisible in the client portal – because they didn’t follow the process for handling non-English documents. This poor customer service was entirely preventable and, though I’m not an insurance industry expert, I’m going to tell you how.

I started this article with my personal experience because all service providers need to consider customer impact when designing their AI adoption. Unfortunately for me, health insurance is a relatively inelastic service. The insurance company – let’s start to see ourselves in their position now – has many customers locked in for the year irrespective of individual satisfaction. It also means that customer acquisition is relatively fixed. Insurance companies are not alone in having profit margins that are won and lost in processes. They’re also not alone in having a customer base that includes stubborn engineers who will spend above-average time investigating problems to discover a root cause (hi, that’s me). Even though I can’t switch medical insurance, the original agent’s mistakes followed by my persistence led to an undesirably high touch time for the insurance company (getting personal again, I digress…)

Whether your organization manages insurance claims, manufactures automotive components, or facilitates the food and beverage supply chain, profitability is influenced by how well your people, processes and systems are harmonized. Fortunately, some of the up-and-coming solutions embedded with AI have started to measurably improve the balance with people, processes and, ultimately, profit. One of the solutions with a high yield potential from relatively low effort is called Process Mining. Gartner defines it as “a technique designed to discover, monitor and improve real processes (i.e., not assumed processes) by extracting readily available knowledge from the event logs of information systems”. What gives process mining the potential for high yield with low effort is that it leverages information that your business processes already generate but traditionally ignore outside of IT troubleshooting. Process mining users are provided with unprecedented visibility of process flows and deviations. Analysis of those deviations turns into data-driven continuous improvement with the possibility of incorporating process improvements that were already proven through execution even though they weren’t pre-planned.

To Know More, Read Full Article @ https://ai-techpark.com/ais-role-in-process-mining/

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In the digital world, data is the lifeline of any business, be it trade secrets, sales records, customers’ personal data, and other sensitive information. Organizations use this data to create innovations and increase their long-term client base.

However, the current situation is quite different, especially with this surge in cyberattacks, insider threats, and phishing attacks. In a recent report by Forbes, it was witnessed that in 2023, security breaches saw a 72% increase from 2021, which held the previous record. Hence, protecting this data has never been so important.

Organizations can use data loss prevention (DLP), an indispensable tool that monitors, identifies, and protects sensitive data from unauthorized access and leakage, to prevent data loss.

DLP also aids organizations in meeting regulatory mandates such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). These laws and regulations are stringent obligations in an organization that secures sensitive data and notifies the security teams during data breaches. With the help of DLP solutions, CISOs, CIOs, or IT managers can ensure that the right employees are accessing the right data for the correct reason.

For a better understanding of this subject, today’s AITech Park article will discuss data loss prevention, how it functions, software solutions, and the latest strategies and policies organizations can implement for stronger data security.

Reasons for Data Loss in Organizations

With the growing digital data and increasingly sophisticated cyber threats, data loss has become a primary concern for organizations worldwide, and data breaches, data leakage, or data exfiltration commonly cause this data loss.

Cybercriminals steal and transfer data from a network or device in data exfiltration. This act can be conducted by insiders or outsiders who generally perform cyberattacks such as DDoS attacks or phishing, and such data are exfiltrated through login credentials and intellectual property.

insider threats are extremely dangerous because the hazards come from within the company, leaving sensitive data vulnerable to exploitation. According to the website Check Point, it was observed that 43% of all breaches are insider threats, either intentional or unintentional, through company employees or former employees, contractors, and business associates.

It is witnessed that breaches often occur due to employees’s negligence, and there are numerous reasons such as weak security practices, execution of poor cybersecurity training programs, and not applying the principle of least privilege (POLP). Therefore, organizations need to provide comprehensive cybersecurity training for their employees so they comprehend the significance of keeping company data and personal data safe from antagonists.

CISOs, CIOs, or IT managers should also focus on creating strategies around DLP solutions and train employees to adopt cybersecurity best practices when performing their work.

To Know More, Read Full Article @ https://ai-techpark.com/data-loss-prevention-in-digital-world/

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As cyberattacks advance in their sophistication and frequency, traditional cybersecurity defenders-the firewalls, antivirus software, even intrusion detection systems-are no longer sufficient in protecting companies. Organizations are bound to face advanced persistent threats (APTs), ransomware, as well as insider attacks in 2024 that often go undetected by automated detection tools. This makes proactive cybersecurity a dire necessity.

According to new research findings, the average amount of time taken before it is possible to detect a breach stands at more than 200 days, which is a very long window for cyberthieves to siphon sensitive data and cripple business operations.

This mainly occurs in B2B organizations operating within the finance, healthcare, and technology sectors, as these sectors are mainly characterized by sophisticated attackers seeking high-value data. However, the only solution is in cyber threat hunting-a proactive security approach aimed at detecting threats before they trigger damage.

In the guide here, we will cover the most important steps to implement a robust cyber threat hunting strategy tailored for 2024-overview of all the skills, processes, and technologies that will help in keeping your business safe.

What is Cyber Threat Hunting?

Cyber threat hunting is one of the proactive cyber security practice wherein the trained and well-equipped security analysts proactively search for hidden or undetected threats within an organization’s network.  While the traditional monitoring systems passively wait for alerts, the threat hunters search for malicious activity or a weakness that can be exploited.

Why It Matters in 2024

Today, the threat landscape for cyber defence is no longer passive but active detection. Attackers are continually evolving by attempting to evade detection with tactics like lateral movement, credential dumping, and fileless malware. Threat hunting becomes very critical in this approach since it looks beyond waiting for automated tools to flag an anomaly and instead hunts for and discovers sophisticated attacks made to evade traditional defenses.

Common Cyber Threats in 2024

Some of the prominent threats businesses will face in 2024 include the following:

Advanced Persistent Threats (APTs): Organized cyberattacks that siphon off data for long periods of time without being detected.

Ransomware: A ransomware attack encrypts a victim’s data and demands payment in lieu of providing decryption keys.

Insider Threats: It is an employee or contractor who intends to do evil or shows malacious carelessness in doing his duty that might lead to security breaches.

Zero-Day Exploits: In this case, attacks exploit vulnerabilities that have not been patched yet.

To Know More, Read Full Article @ https://ai-techpark.com/implementing-cybersecurity-threat-hunting/

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In 2024, the cybersecurity realm has opened new doors to new vulnerabilities and attack techniques. As attacks become more sophisticated and dynamic, traditional defense mechanisms fail to provide protection. Therefore, to effectively combat these challenges, CISOs and IT leaders need to analyze the current situation and mitigate threats in real-time.

As we look ahead to 2025, likely, the concerns faced by CISOs and IT leaders in 2024 will potentially worsen.

However, for a handy deep dive into tackling cyber attackers, this roundup of AITech Park articles on the cybersecurity topic offers guidance in creating good cyber awareness strategies, insights, and recommendations that will aid in embedding privacy compliance into your culture.

The Rise of Cybersecurity Careers

As cyberattacks become increasingly sophisticated, organizations are prioritizing the hiring of certified cybersecurity professionals to enhance their security measures. Therefore, to excel in this ever-evolving competitive field, it is crucial to pursue the right certification courses. In 2024, the top popular cybersecurity certifications include CompTIA Security+, OSCP, CISA, CISSP, and CISM. Each certification course offers valuable skills and knowledge catering to numerous roles within cybersecurity.

Understanding the Third-Party Risk Management Strategies

In this new-age world, third-party risk management strategies have become quite essential in this modern interconnected business environment. As businesses no longer rely solely on an organization’s security, CISOs require external connections to manage security strategies. Therefore, implementing robust third-party cyber risk management so you can continuously focus on due diligence, monitoring, deception, and incident response plans can help limit your exposure and defend against growing threats.

Preparing for Data Center Security Threats in 2024

Most organizations have data centers that are rich in critical information, and for cybercriminals, this center is the prime target. Therefore, IT leaders must prioritize building the defenses around to eliminate the increasing ransomware and cyberattacks. This also implies that hardware-based root-of-trust (RoT) systems should be combined with AI technologies that will ultimately enhance zero-trust practices beyond current capabilities.

The need of the hour is a comprehensive cybersecurity strategy that will secure the organization’s digital assets and reduce the risk of loss, theft, or destruction of company data or systems. Hence, by reading the recommended articles, you can create a robust strategy that will protect your brand from reputational harm and create a safe environment for employees, stakeholders, and the organization.

To Know More, Read Full Article @ https://ai-techpark.com/top-cybersecurity-articles-in-2024/

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