Tag: Cybersecurity

Businesses constantly juggle competing priorities—such as balancing innovation with reliability, investment with profit, and speed with security. Each leader in the organization plays a role in these decisions, and traditionally, CISOs have been positioned as the defenders of the business, focused on security and risk mitigation.

Over the past decade, however, the role of the Chief Information Security Officer (CISO) has evolved in response to the increasing digitization and data-driven nature of business. No longer confined to back-office support, CISOs are stepping into more prominent roles, contributing to broader strategic discussions and decision-making.

Recent data from Netskope’s Bringing Balance report highlights this transformation. The modern CISO has emerged from the shadows of the executive team to become an active participant in driving business objectives, fostering innovation, and enabling growth.

According to the report, 59% of CISOs now view themselves as business enablers, and 67% express a desire to take on an even more active role. Yet, challenges remain: 65% of CISOs believe their C-suite peers don’t fully recognize their role in enabling innovation, and 92% cite conflicting risk appetites as a significant barrier within the executive team.

So, how can CISOs reshape perceptions and demonstrate their value as key enablers of business success?

Linking Cybersecurity to Business Goals

In today’s data-centric world, the CISO’s role is critical to ensuring operational continuity. While their contributions may not directly generate revenue, their impact becomes evident when considering how uncontrolled risks could hinder consistent revenue generation.

To bridge this gap, CISOs must build relationships across departments to understand their unique priorities and align security policies with organizational goals. This isn’t just about mitigating risks—it’s about enabling success. By fostering collaboration across silos, CISOs can transition from being purely defensive to becoming proactive, progressive, and solutions-focused.

Research shows that 66% of CISOs want to adopt a more positive approach, asking, “Yes – how do we achieve this?” instead of defaulting to restrictions. This mindset shift is key to driving alignment with broader business objectives.

To Know More, Read Full Article @ https://ai-techpark.com/role-of-cisos-within-c-suite/

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In today’s era of distributed work, the seamless delivery of borderless digital applications and services is essential. Powered by the cloud and secured through high-performance networks, these solutions must provide best-in-class user experiences.

To achieve this, organizations require real-time visibility and insights into data flows across their extended enterprise networks. Coupled with robust People, Process, and Technology safeguards, this ensures data security both in transit and at rest. These measures are critical for protecting data, maintaining privacy, complying with regulatory mandates, and mitigating enterprise risks.

What is Network Observability?

Network observability is a strategic technology enabler designed to address these challenges. It goes beyond passive monitoring, offering dynamic visibility, actionable insights, and visualization of the network’s state. By bridging network operations and cybersecurity, it enhances resilience, reliability, and trust, aligning with business goals, user experience, and ecosystem requirements.

At its core, network observability leverages advanced mechanisms such as data lakes and AI for real-time analysis of large-scale data. Automation and orchestration enable proactive alerts, streamlined troubleshooting, and continuous improvement through trend analysis and retrospective insights.

Foundational Elements of Network Observability:

Telemetry: Real-time data collection from diverse sources enables a comprehensive understanding of the network’s operational health.

Data Lakes: Context-rich telemetry data is processed and analyzed to deliver actionable insights, fostering collaboration between network and security teams.

Visibility & Visualization: Real-time insights, historical trend analysis, and data correlation drive continuous network optimization.

Automation: AI-powered workflows ensure proactive service assurance and incident management.

The Value of Network Observability:

Managing network performance and ensuring a superior user experience are crucial in today’s distributed, borderless environment. As applications shift from centralized data centers to global cloud infrastructures, the modern network has evolved into a complex and dynamic system. This transformation has amplified the need for continuous, dynamic network visibility to ensure alignment with service-level agreements for availability, performance, and resilience.

As organizations embrace borderless work environments, the cloud serves as a critical hub for application delivery, while the network is the foundation that enables secure, high-performance operations. Network observability is mission-critical for fostering trust, ensuring data protection, and achieving regulatory compliance.

To Know More, Read Full Article @ https://ai-techpark.com/rise-of-network-observability/

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In a turn of events worthy of a sci-fi thriller, Large Language Models (LLMs) have surged in popularity over the past few years, demonstrating the adaptability of a seasoned performer and the intellectual depth of a subject matter expert.

These advanced AI models, powered by immense datasets and cutting-edge algorithms, have transformed basic queries into engaging narratives and mundane reports into compelling insights. Their impact is so significant that, according to a recent McKinsey survey, nearly 65% of organizations now utilize AI in at least one business function, with LLMs playing a pivotal role in this wave of adoption.

But are LLMs truly infallible? This question arose in June when we highlighted in a blog post how LLMs failed at seemingly simple tasks, such as counting the occurrences of a specific letter in a word like strawberry.

So, what’s the real story here? Are LLMs flawed? Is there more beneath the surface? Most importantly, can these vulnerabilities be exploited by malicious actors?

Let’s explore the top five ways in which LLMs can be exploited, shedding light on the risks and their implications.

Data Inference Attacks

Hackers can exploit LLMs by analyzing their outputs in response to specific inputs, potentially revealing sensitive details about the training dataset or the underlying algorithms. These insights can then be used to launch further attacks or exploit weaknesses in the model’s design.

Statistical Analysis: Attackers may use statistical techniques to discern patterns or extract inadvertently leaked information from the model’s responses.

Fine-Tuning Exploits: If attackers gain access to a model’s parameters, they can manipulate its behavior, increasing its vulnerability to revealing sensitive data.

Adversarial Inputs: Carefully crafted inputs can trigger specific outputs, exposing information unintentionally embedded in the model.

Membership Inference: This method involves determining whether a specific data sample was part of the model’s training dataset, which can expose proprietary or sensitive information.

As LLMs continue to transform industries with their capabilities, understanding and addressing their vulnerabilities is essential. While the risks are significant, disciplined practices, regular updates, and a commitment to security can ensure the benefits far outweigh the dangers.

Organizations must remain vigilant and proactive, especially in fields like cybersecurity, where the stakes are particularly high. By doing so, they can harness the full potential of LLMs while mitigating the risks posed by malicious actors.

To Know More, Read Full Article @ https://ai-techpark.com/top-2024-llm-risks/

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As technology advances at an unprecedented pace, integrating Internet of Things (IoT) technologies into engineering systems is no longer just a trend; it has become essential. Global industries are under pressure to boost efficiency, lower operational costs, and remain competitive, with IoT emerging as a transformative force, especially in Telecom and Utility sectors.

Challenges in IoT Integration

Despite IoT's vast potential, integrating it into existing engineering systems presents challenges:

Legacy Infrastructure: Older systems often lack the connectivity and flexibility to fully harness IoT solutions.

Data Overload: The vast data produced by IoT devices can overwhelm traditional processing systems, potentially leading to inefficiencies.

Cybersecurity Risks: IoT devices can be vulnerable entry points for attacks, potentially compromising entire networks.

Opportunities with IoT

Even with these challenges, the benefits of IoT are substantial, particularly for Telecom and Utility companies:

Real-Time Monitoring: IoT enables real-time monitoring of critical infrastructure, even in remote locations.

Predictive Maintenance: IoT sensors and analytics facilitate early detection of equipment issues, reducing downtime and optimizing maintenance.

Network Optimization: IoT enhances resource allocation and network quality, delivering better customer service while lowering operational costs.

IoT works best in synergy with complementary technologies. Integrating Data & Analytics, AI/ML, and Cloud Engineering can help overcome IoT challenges. By utilizing advanced data processing tools and AI insights, data from IoT devices can be transformed into actionable intelligence, aiding decisions on energy optimization, network security, and fraud prevention.

Integrating IoT technologies in engineering systems presents a major opportunity for Telecom and Utility companies to transform their operations and drive innovation. By addressing integration challenges and capitalizing on IoT's potential, businesses can achieve new heights of efficiency, resilience, and customer satisfaction. With Techwave’s expertise and advanced solutions, organizations can navigate the complexities of IoT integration and achieve sustained success in an increasingly connected world.

To Know More, Read Full Article @ https://ai-techpark.com/intelligent-infrastructure-with-iot/

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Roy, could you start by sharing your journey from your role in Israeli cyber defense to co-founding Rezonate and your vision for the company?

Yes, thank you for the opportunity to share. I headed Israeli Cyber Defense Operations, where I was responsible for defending Israel’s critical infrastructure and public companies against cyber attacks, cyber terror, and digital espionage. My mission during over 20 years of service was to develop cutting-edge technology and lead cyber defense operations. I was also part of the founding team of NISA, Israel’s equivalent of the NSA, and established alliances with numerous countries and business partners.

I moved to Boston eight years ago to pursue my MBA at MIT Sloan. Following that, I served as the VP of Product Incubation at Cybereason. I founded three market-changing lines of business, including Cybereason’s flagship XDR product and its strategic partnership with Google Cloud. My journey has always been about pushing boundaries and innovating in cybersecurity around new challenges. When we realized that identity was the new perimeter in security operations, we saw that organizations still struggle to protect it. We had many ideas for revolutionizing identity security technology, so we started Rezonate.

What do you see as the most pressing issues currently facing identity access management, and how are these challenges impacting enterprises?

The shift to the cloud has been monumental, even accelerating during economic downturns for cost-saving and efficiency. Traditional endpoint and network-centric security needed to evolve to include cloud-forward environments. Users and machines have become the new focal points for both defenders and attackers. With 85% of attacks stemming from compromised identities, legacy systems cannot sufficiently protect them. That’s why my partner, Ori Amiga, and I saw a unique opportunity to challenge these outdated approaches on both the cybersecurity and identity and access (IAM) sides with an innovative identity-centric security solution, leading to the founding of Rezonate.

Can you explain the role of identity security posture management (ISPM) and its importance in today’s security landscape?

Identity security posture management is critical for reducing your identity attack surface. Understanding what your security controls are, monitoring identities to ensure they have the least privileged access, and being able to remediate access automatically when a risk or threat is detected. ISPM helps you identify and prioritize high-risk identities: Spots accounts with excess privileges, dormant accounts, misconfigured access, and elevated risk profiles. Posture management also streamlines the remediation process. Implementing ISPM supports regulatory compliance, enhances audit performance, and ensures effective policy enforcement. It also helps organizations to continuously monitor security issues and enforce best practices and policies.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-roy-akerman/

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Cybersecurity breaches are increasingly common among small and medium businesses (SMBs), making them ideal targets for cybercriminals. Due to limited budgets, lack of expertise, and the misconception of being “too small to be targeted,” many SMBs operate with minimal cybersecurity defenses. Unfortunately, this vulnerability is exactly what cyber attackers rely on, exploiting weak defenses through relentless attacks and sophisticated phishing campaigns. In India alone, ransomware attacks on websites surged by 261% this year, with insurance companies often left covering the damage.

This raises an important question: How are organizations that specialize in risk management being blindsided by cyber threats? The answer lies in inadequate security practices. Many businesses lack critical defenses such as multi-factor authentication (MFA), phishing-resistant employee training, reliable backups, and endpoint detection and response (EDR) systems. Additionally, the rapid shift of SMBs to cloud platforms introduces complex configurations that exceed the technical capacity of many small businesses.

Insurance Companies and Cyber Risk Management

Recognizing the growing risks, insurers are tightening their standards for cyber insurance policies. Companies now need to meet stricter requirements, such as multi-step authentication (e.g., verification codes via email or phone), routine security scans, and offline data backups, to qualify for coverage.

However, insurers face challenges in evaluating cyber risk. Without access to comprehensive insights from businesses' security systems, insurance companies struggle to assess the true level of exposure. This reactive, “outside-looking-in” approach slows incident response efforts, as forensic teams must first reconstruct pre-breach system conditions. At the same time, businesses without a solid cybersecurity framework increase uncertainty for insurers, forcing them to reconsider how they assess and manage cyber risk.

The integration of cybersecurity and insurance efforts creates a win-win-win scenario for all stakeholders. Insurance companies benefit from fewer claims and improved financial performance, SMBs enjoy better protection and more favorable policy terms, and end-users gain enhanced data security.

By fostering partnerships between insurers and cybersecurity providers, the industry can shift from reactive risk management to proactive prevention. This collaborative approach will not only help mitigate the growing ransomware threat but also create a more resilient digital environment for businesses and consumers alike.

To Know More, Read Full Article @ https://ai-techpark.com/role-of-insurance-in-cyber-defense-enhancement/

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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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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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Yashin, to kick things off, could you share what inspired you to transition from a career in academia and engineering to founding Pvotal Technologies?

Growing up, I thought a lack of proper education was the root of many societal issues and inefficiencies.

Idealistically, I entered academia thinking I could become a professor who would nurture the issues leading to a wavering generation of talent, innovation, and development. Unfortunately, I quickly realized how some processes were limiting, stifling, and stuck in an antiquated age.

I could not build or address problems I saw in my niche field due to software issues, data breaches, the high cost of licensing fees for some critical tools, and the poor integration of tools. These issues led me to lose thousands of hours in frustration fixing technical problems rather than focusing on my growth, thesis, and research. The tools I used became a greater source of frustration than my research, constantly distracting me from my objectives.

My skills and resolve were too limited to reform academia from within, so I decided to focus on the issues within the software industry to limit the problems that more talented academics faced. I co-founded Pvotal with Ashley to build a new generation of solutions that helped customers focus on the value they bring to customers rather than get stuck in an iterative cycle of integrating code and debugging updates.

Pvotal emphasizes creating “Infinite Enterprises.” Could you explain what this concept entails and how it aligns with your overall mission?

While many industries have adopted different interpretations of the ideal Infinite Enterprise, we believe the “infinite enterprise” is any company that has achieved an infinitely scalable, independent, resilient, and secure infrastructure. Once these criteria are met, we observed that it allows businesses to truly innovate, improve, and elevate their value proposition to customers.

The age-old adage of teens or some fresh graduates going into “founder mode” can build the next generation of software in their proverbial garage, shared workspace, or dorm room is simply no longer possible.

The rise of hyperspecialization, wanton integration of third-party code or vendors, and the unmanaged accumulation of technical debt has led most software companies to become antiquated, vulnerable, and overbloated pieces of code that can no longer efficiently protect their customers’ data, provide a competitive edge to their users, and have a reasonable cost/utilization footprint.

Most modern enterprise software has at least 17 paid or free SaaS, PaaS, and third-party code powering its operation or development. With a tough economy, inflation, and squeezed supply chains, these different services are forced to raise prices continually, thus shifting the burden on the end consumer. In addition to the increasing costs, these software are often abandoned or introduce vulnerabilities to the enterprise supply chains, which is why we have experienced a record-breaking number of successful cyberattacks, ransomware, and fraud every year for the past decade.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-yashin-manraj/

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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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