Tag: AItech Article

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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Picture a world where your professional services business operates like a well-oiled machine, effortlessly balancing resources, predicting project outcomes, and communicating with precision. This isn’t a far-off dream—it’s the reality that Artificial Intelligence (AI) is bringing to the professional services industry right now. As the lines between human expertise and technological capabilities blur, AI is emerging as the secret weapon for firms looking to surge ahead in a fiercely competitive market.

By leveraging AI-powered Professional Services Automation (PSA) software, firms can optimize resource allocation, enhance governance, and streamline communication processes. Let’s explore how AI can revolutionize three key areas of professional services operations.

Intelligent Resource Management

One of the most significant challenges faced by professional services firms is effective resource management. AI-powered solutions can provide unprecedented insights and automation in this critical area:

Optimizing Team Utilization

AI algorithms can analyze historical project data, current workloads, and upcoming commitments to balance the workload across team members. This ensures that no individual is overworked while maximizing overall team productivity. By continuously monitoring utilization rates, AI can alert managers when team members are approaching burnout or when there’s capacity for additional projects.

Forecasting and Capacity Planning

Predictive AI models can forecast resource requirements for upcoming projects based on past performance data and project characteristics. This allows firms to anticipate staffing needs, plan for hiring or training, and make informed decisions about taking on new projects. AI can also help identify potential resource conflicts well in advance, giving managers time to reallocate resources or adjust timelines.

Skill Matching and Project Staffing

AI can analyze the skill sets of available resources and match them with the requirements of incoming projects. This ensures that the right people with the right expertise are assigned to each project, improving project outcomes and client satisfaction. Additionally, AI can identify skill gaps within the organization and suggest upskilling opportunities to prepare teams for future high-stakes projects.

Profitability Analysis and Pricing Optimization

By analyzing PSA tool data, AI can uncover patterns in project profitability across different types of engagements, clients, or project phases. This insight allows firms to identify which areas of their business are most profitable and which may be losing money. AI can then suggest optimal pricing strategies for different types of projects or clients, helping firms maintain a healthy balance between competitive pricing and profitability.

To Know More, Read Full Article @ https://ai-techpark.com/revolutionizing-professional-services-with-ai/

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In today’s fast-paced business landscape, data is not just an asset; it’s a cornerstone of strategic decision-making. For B2B companies, leveraging data effectively can create significant competitive advantages, enabling them to understand their customers better, streamline operations, and drive innovation. This article explores the importance of a robust data strategy and how businesses can harness data to outpace their competition.

The Value of a Strong Data Strategy in B2B

Why Data is the New Competitive Currency

As businesses increasingly rely on data to inform their decisions, it has become the new competitive currency. Companies that effectively harness data can unlock valuable insights that guide product development, enhance customer experiences, and optimize operational efficiency. For instance, consider how a leading B2B SaaS company used data analytics to analyze customer usage patterns, which led to the development of new features that directly addressed user needs, resulting in a significant boost in customer retention.

Aligning Data Strategy with Business Goals

A successful data strategy must align with the overarching business objectives. Organizations should ensure that their data initiatives are not just about collection but are focused on measurable outcomes. For example, a manufacturing company may set specific targets for reducing downtime by analyzing equipment performance data. By aligning data strategy with business goals, companies can demonstrate clear ROI and reinforce the value of data initiatives across the organization.

Key Components of a Robust Data Strategy

Data Collection and Management

Effective data collection is the foundation of any data strategy. B2B organizations must prioritize collecting relevant and high-quality data from diverse sources, such as customer interactions, market research, and internal processes. Additionally, centralized data storage solutions, such as data lakes or warehouses, can streamline data management and improve access across departments.

Implementing robust data governance is equally essential. Establishing clear policies on data usage, ownership, and security ensures that data remains accurate, reliable, and compliant with regulations. This not only enhances decision-making but also builds trust among stakeholders who rely on data for strategic insights.

In an era where data is a vital asset, developing a robust data strategy is crucial for B2B organizations seeking a competitive edge. By aligning data initiatives with business goals, implementing best practices, and leveraging advanced tools, companies can harness the power of data to drive growth, enhance customer experiences, and remain agile in a dynamic marketplace. Embracing a culture of data-driven decision-making will not only empower organizations to thrive but also position them as leaders in their industries.

To Know More, Read Full Article @ https://ai-techpark.com/data-strategy-competitive-advantage/

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Chief Information Security Officers (CISOs) have some serious responsibilities on their shoulders as they single-handedly carry the security policies and enforcement, which are directly proportionate to an entire company’s success or downfall.

CISO’s insights and knowledge allow a company to balance out supporting its internal team while guarding the organization’s data and infrastructure.

However, in recent years, CISOs have witnessed shifts in the cybersecurity realm; especially with the technological advancements, cyberattacks such as phishing have increased by 58%, consisting of 90% data and 42% malware and ransomware attacks, affecting millions of users yearly (Cisco).

Even with the latest security protocols and software, it is only possible to fully protect against cyber threats with proper security awareness and strategies.

Therefore, to protect your company from an avoidable phishing attempt, AITech Park brings you a comprehensive guide on the different types of phishing attacks and how CISOs and their internal teams can handle them.

For a better understanding, let’s dive into the different types of new-age phishing attacks:

Email Phishing

Email phishing is the oldest and most common form of phishing, where scammers send spam emails to as many people as possible, hoping that a fraction of the targets fall for the attack. As per a recent study by Deloitte, it was witnessed that 91% of cyberattacks begin with email phishing and 32% of successful breaches involve the use of phishing techniques.

Cyberattackers often impersonate any well-known or legitimate brands and target their victim through those brands.

How to Spot Spam Emails?

Scammers often write email subject lines that are more appealing with strong emotions or create a sense of urgency. The body of the email instructs the recipient to take reasonable actions that deal with sensitive information or downloading malware. For instance, a phishing link might read, “Click here to update your profile.” When the victim clicks that malicious link, it takes them to a fake website that embezzles their login credentials.

Deepfake Scams

With rapid development in AI technology, deepfake has become more accessible to users. In recent research by Egress, 63% of cybersecurity personnel surveyed were worried about the cyber attacks introduced by deepfakes. To battle these types of attacks, CISOs can use deepfake detection tools that are available on the internet. These tools can point out synthetic images generated by AI and ML technologies, leaving unique traces that are invisible to the human eye. For instance, in recent years there have been modified videos of popular dignitaries that are common on social media platforms; these videos can be with a fun intention or sometimes defaming them through manipulated speeches or actions.

To Know More, Read Full Article @ https://ai-techpark.com/risks-of-enhanced-phishing/

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How AI avatars are transforming customer service in business

What hasn’t AI transformed? Many business fields have evolved in recent years thanks to the tools that Artificial Intelligence has provided, one of them is customer service, through the use of AI avatars.

This is a new way in which companies are interacting with their public, trying to be more efficient and sophisticated in their processes. AI avatars are a very interesting figure in this transformation of customer service, as they have allowed companies to interact with their customers in a more personalized and efficient way than ever.

These virtual assistants are equipped with natural language processing and machine learning technology, which has redefined the customer experience by providing faster responses, solving problems more accurately, and even anticipating potential customer needs.

In this article we will delve deeper into the topic of AI avatars, how they are changing the rules of the game in customer service and what they bring to today’s modern companies.

Let’s start with the basics: What is an AI avatar?

When we talk about an AI avatar, we refer to the virtual, dynamic and animated representation of a person through Artificial Intelligence to interact with users in an automated and personalized way.

These avatars have the ability to simulate a human conversation, answer questions, provide assistance and solve problems, without the need for direct human intervention.

The digital avatar market is growing considerably

Everything indicates that the strategy of digital avatars is not a passing fad but is here to stay and grow constantly. According to a report by Spherical Insights, the global digital avatar market size is expected to reach $283.47 billion by 2032, a clear sign that businesses are increasingly investing in this tool.

“Technological advances in 3D modeling, animation and rendering result in increasingly realistic and visually appealing digital humans. Conversations become more attractive and closer because these avatars can look very similar to humans,” they noted in another report from the Market Research Future portal.

In this report they also indicate that the main market that uses AI avatars is North America, driven by the launch of new products and expansions of others, which has increased the need for this virtual assistance. Likewise, the Asia-Pacific region is the fastest growing region in terms of AI avatars, which is very promising for the immediate future.

To Know More, Read Full Article @ https://ai-techpark.com/transforming-customer-service-with-ai-avatars/

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There is growing concern over deepfakes, which are videos and audios that are highly realistic yet fake across various industries, but perhaps more pertinent in the B2B context. These synthetic media can mislead society and create negative impacts on reputation and financial aspects. However, it is evident that social media platforms have an essential role in addressing the fake problem and enhancing the credibility of online interactions as enterprises operate in this challenging environment. This article looks at the rise of deep fakes and also explores how popular social media companies are responding to this problem.

Understanding Deepfakes

Deepfakes are a form of synthetic media that apply artificial intelligence and machine learning to generate hyper-realistic fake audiovisual data. This technology relies on neural networks, and particularly on generative adversarial networks (GANs), to create realistic modifications of existing media.

The first step involves the accumulation of massive data sets that include images, videos, and even voice clips of the targeted person. These datasets enable AI to capture the details of the person’s gestures, voice, and even their tone. For example, GANs are composed of two neural networks, including a generator and a discriminator. The generator thus generates fake content, and the discriminator compares it with real media. This process is carried out in a cycle where the generator generates outputs until the results are as real as the original content being emulated.

Deepfakes can accommodate a range of manipulations based on simple swaps of facial images in videos to advanced ways of forgery where a person looks and acts like doing something they never did. It can also be applied where someone’s voice is changed to say sentences he has never said. This level of realism presents some problems in differentiating between real media and fakes, which could perpetuate skepticism and distrust of digital media.

Social media platforms are at the forefront of the fight against deepfakes, serving as essential gatekeepers to maintain the integrity of online communication. As the sophistication of deepfake technology rapidly evolves, these platforms face the growing challenge of detecting and mitigating manipulated content before it spreads. Their role is critical, not just in protecting users from deception but also in preserving trust across digital spaces where businesses interact with clients, stakeholders, and the public.

For companies, the stakes are equally high. Deepfakes can significantly damage brand reputation and sow confusion, eroding the trust that is central to B2B relationships. Businesses must be vigilant, ensuring they remain informed about the latest developments in deepfake technology and taking proactive steps to defend against its potential harms. By adopting a strategy that includes close collaboration with social media platforms, regular updates to security protocols, and internal training on identifying manipulated content, companies can safeguard their reputation and maintain the trust of their audience.

To Know More, Read Full Article @ https://ai-techpark.com/role-of-social-media-platforms-in-combating-deepfakes/

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In recent years, computer vision (CV) has appeared as a transformative technology that reshapes the landscape of numerous industries by allowing machines to analyze and understand visual information around them.

According to tech leaders, computer vision is often referred to as the eyes of artificial intelligence (AI), which makes it a transformative technology that not only revolutionizes the industries that adapted it but also becomes a cornerstone for the advancement of AI. With more technological advancements, the convergence of CV with IoT, big data analytics (BDA), and automation has given rise to smart work that remains competitive and improves productivity and efficiency.

In this blog, we will learn about the critical role that computer vision plays in pushing the boundaries and creating new avenues for different industries in this digital world.

The Core of Computer Vision

Computer vision is a field of study that enables computers to replicate human visual systems and is often considered a subset of artificial intelligence that collects information from digital images and videos and further processes it to define different attributes. CV relies on way recognition approaches to self-train and comprehend visual data. Earlier ML algorithms were used for computer vision applications; now deep learning (DL) methods have developed as a better solution for this domain. Therefore, with more training with data and algorithms, CV now works much the same as human vision.

These capabilities make computer vision more useful in different industries that range from healthcare and logistics to manufacturing and financial services.

Computer Vision Use Cases

Computer vision technology has tremendous potential to revolutionize numerous industries by providing an automated technique to identify minute defects in products. With the help of ML algorithms, computer vision systems can detect slight variations in outcome quality that may not be observable by the human eye.

The healthcare industry has already advanced with new-age robotic surgeries, but computer vision has quite a multifold effect that can help in performing even delicate and complex procedures. According to a recent report by Statista, more than 20.21% of healthcare institutions and hospitals are implementing CV in their daily processes. This technology can be improved by real-time, high-resolution photographs of the surgical site, allowing the surgeon to have a better idea and acquaintance with the procedure.

Computer vision is an area that tech researchers are still researching and looking for further development in. As we navigate into the future of intelligent technologies, computer vision can redefine boundaries that machines can archive and further open new doors to new possibilities that will reshape the way we interact with the world around us.

To Know More, Read Full Article @ https://ai-techpark.com/computer-vision-in-different-industries/

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Artificial intelligence has already had a positive effect on several industries, but unfortunately, this popularity and success have caused some wrongdoers to attempt to capitalize on the AI boom in unethical and illegitimate ways. One such practice is known as “AI washing,” and it is arguably one of the biggest threats to the continued growth of AI.

AI washing is most easily understood by comparing it to the similar practice of greenwashing, in which companies misrepresent their products as being more eco-friendly than they actually are. Similarly, AI washing involves making false representations of a product or service’s use of artificial intelligence technology. Through this deceit, businesses are riding the wave of AI hype without offering their customers the benefits.

Understanding AI washing

One of the most common examples of AI washing takes advantage of many consumers’ lack of knowledge about artificial intelligence with misleading product descriptions. For example, a business could claim that traditional algorithms are artificial intelligence, yet because of the similarities between the two technologies, the average consumer might not realize they are being misguided.

Some businesses are guilty of a form of AI washing in which they exaggerate the scale of the capabilities or use of AI as it relates to their business. For example, a company might claim to offer “AI-powered services” when, in reality, it only uses artificial intelligence in ways incidental to its business. Even though these businesses do use AI to some extent, they have still misled the consumer into believing that their use is more extensive than it actually is.

Other businesses may claim to use artificial intelligence without substantially implementing it into their business. Some have claimed to use AI without using it at all, while others claim to use it while it’s still in its early stages of development and has no noticeable effects.

To Know More, Read Full Article @ https://ai-techpark.com/combatting-ai-washing-threat/

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Companies are dealing with overwhelming amounts of data, and this data must be governed, compliant, and secure, especially when working in the financial, healthcare, and insurance sectors. As the complexity of data environments increases, traditional data governance approaches largely fail to address these challenges adequately and lead to the emergence of what many researchers refer to as Data Governance 2.0. undefined Laying its foundation is the metadata-driven data fabric, which represents a highly transformative approach to data management and governance, compliance, and security.

Expanding on the concept of data fabric architecture and elements, this article focuses specifically on the use of metadata layers to improve governance and compliance for businesses operating in highly regulated environments.

In this blog, we will also discuss the concepts, opportunities, and risks of constructing a metadata-driven data fabric to enhance compliance and security.

The Evolution of Data Governance: From 1.0 to 2.0

Data Governance 1.0: Legacy Governance Models

The conventional view of the data governance process was mainly concerned with data adequacy, control, compliance, and the ability to store data securely in isolated databases. This was primarily a rule-governed and manual approach. The governance policies we had were far from dynamic and flexible to adapt to the evolving needs of the current organizations.

Legacy systems in Data Governance 1.0 face several limitations:

Manual processes: Some of the measures of security are checked manually, and this leads to slow processes and errors because it is done by human beings.

Siloed data: Data resides in multiple systems and silos, which causes issues with governance alignment.

Static policies: Governance rules do not adapt to the emergence of new data scenarios and the constantly evolving compliance requirements.

Why Data Governance 2.0?

The data environment has changed, and it is now imperative for organisations to sort data through hybrid and multi-cloud solutions, and address increasing concerns of compliance and security. This phenomenon is has therefore resulted to what is now known as Data Governance 2. 0, a governance model designed for the modern data ecosystem, characterized by:

Real-time governance: Managing a multilayered set of governance policies for both cloud and on-premises & hybrid solutions.

Data integration: Integration management of distributed data and assets with out leaving their original location.

Proactive compliance: Engaging metadata and AI to enforce compliance in a dynamic manner.

To Know More, Read Full Article @ https://ai-techpark.com/how-metadata-driven-data-fabric-ensures-compliance-and-security/

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