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As Saudi Arabia accelerates its digital transformation, cybersecurity has become a cornerstone of its national strategy. With the rise of digital technologies, from smart cities to advanced cloud infrastructure, the Kingdom recognizes that protecting its digital landscape is paramount to safeguarding its economic future and national security.
But when it comes to cybersecurity, AI has become a double-edged sword. While poised to fortify the security posture of organizations, it has also changed the nature of cyberattacks. While LLMs are trained on large amounts of information, they have expanded the attack surface for businesses.
Singapore has rolled out new cybersecurity measures to safeguard AI systems against traditional threats like supply chain attacks and emerging risks such as adversarial machinelearning, including data poisoning and evasion attacks.
For others, it may simply be a matter of integrating AI into internal operations to improve decision-making and bolster security with stronger fraud detection. With the rise of AI and data-driven decision-making, new regulations like the EU ArtificialIntelligence Act and potential federal AI legislation in the U.S.
Many organizations are dipping their toes into machinelearning and artificialintelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machinelearning projects?
As enterprises scale their digital transformation journeys, they face the dual challenge of managing vast, complex datasets while maintaining agility and security. With machinelearning, these processes can be refined over time and anomalies can be predicted before they arise. This reduces manual errors and accelerates insights.
Unsurprisingly, this is leading to staff frustration and burnout, dissatisfied end users and persistent security vulnerabilities. The reasons include more software deployments, network reliability problems, security incidents/outages, and a rise in remote working. These technologies handle ticket classification, improving accuracy.
As ArtificialIntelligence (AI)-powered cyber threats surge, INE Security , a global leader in cybersecurity training and certification, is launching a new initiative to help organizations rethink cybersecurity training and workforce development. However, this shift also presents risks.
The Austin, Texas-based startup has developed a platform that uses artificialintelligence and machinelearning trained on ransomware to reverse the effects of a ransomware attack — making sure businesses’ operations are never actually impacted by an attack. Valuation Illustration: Dom Guzman
In this special edition, we’ve selected the most-read Cybersecurity Snapshot items about AI security this year. ICYMI the first time around, check out this roundup of data points, tips and trends about secure AI deployment; shadow AI; AI threat detection; AI risks; AI governance; AI cybersecurity uses — and more.
Our commitment to customer excellence has been instrumental to Mastercard’s success, culminating in a CIO 100 award this year for our project connecting technology to customer excellence utilizing artificialintelligence. We live in an age of miracles. When a customer needs help, how fast can our team get it to the right person?
In the quest to reach the full potential of artificialintelligence (AI) and machinelearning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.
As policymakers across the globe approach regulating artificialintelligence (AI), there is an emerging and welcomed discussion around the importance of securing AI systems themselves. A key pillar of this work has been the development of a GenAI cybersecurity framework, comprising five core security aspects.
TRECIG, a cybersecurity and IT consulting firm, will spend more on IT in 2025 as it invests more in advanced technologies such as artificialintelligence, machinelearning, and cloud computing, says Roy Rucker Sr., CEO and president there. The company will still prioritize IT innovation, however.
Whether it’s a financial services firm looking to build a personalized virtual assistant or an insurance company in need of ML models capable of identifying potential fraud, artificialintelligence (AI) is primed to transform nearly every industry. Before we go further, let’s quickly define what we mean by each of these terms.
ArtificialIntelligence (AI), a term once relegated to science fiction, is now driving an unprecedented revolution in business technology. Other key uses include fraud detection, cybersecurity, and image/speech recognition. Respondents rank data security as the top concern for AI workloads, followed closely by data quality.
From the launch of its mobile banking app in 2020 to the enhancement of its internet banking services, ADIB-Egypt has consistently focused on providing convenient, secure, and user-friendly digital banking solutions. Artificialintelligence is set to play a key role in ADIB-Egypts digital transformation.
As a result, many companies are now more exposed to security vulnerabilities, legal risks, and potential downstream costs. Data scientists and AI engineers have so many variables to consider across the machinelearning (ML) lifecycle to prevent models from degrading over time.
Generative AI, when combined with predictive modeling and machinelearning, can unlock higher-order value creation beyond productivity and efficiency, including accretive revenue and customer engagement, Collins says. Drafting and implementing a clear threat assessment and disaster recovery plan will be critical.
Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificialintelligence. The underpinning architecture needs to include event-streaming technology, high-performing databases, and machinelearning feature stores.
AI and MachineLearning will drive innovation across the government, healthcare, and banking/financial services sectors, strongly focusing on generative AI and ethical regulation. Cybersecurity will be critical, with AI-driven threat detection and public-private collaboration safeguarding digital assets.
{{interview_audio_title}} 00:00 00:00 Volume Slider 10s 10s 10s 10s Seek Slider Like legacy security tools, such as traditional firewalls and signature-based antivirus software, organizations that have more traditional (and potentially more vulnerable) SOCs are struggling to keep pace with the increasing volume and sophistication of threats.
Artificialintelligence (AI) has rapidly shifted from buzz to business necessity over the past yearsomething Zscaler has seen firsthand while pioneering AI-powered solutions and tracking enterprise AI/ML activity in the worlds largest security cloud. billion AI/ML transactions in the Zscaler Zero Trust Exchange.
They want to expand their use of artificialintelligence, deliver more value from those AI investments, further boost employee productivity, drive more efficiencies, improve resiliency, expand their transformation efforts, and more. I am excited about the potential of generative AI, particularly in the security space, she says.
As such, cloud security is emerging from its tumultuous teenage years into a more mature phase. The initial growing pains of rapid adoption and security challenges are giving way to more sophisticated, purpose-built security solutions. This alarming upward trend highlights the urgent need for robust cloud security measures.
INE Security , a global cybersecurity training and certification provider, recently launched initiatives with several higher education institutions in an ongoing campaign to invest in the education of aspiring cybersecurity professionals. million, highlighting the severe economic impact of these incidents.
The promised land of AI transformation poses a dilemma for security teams as the new technology brings both opportunities and yet more threat. At the same time, machinelearning is playing an ever-more important role in helping enterprises combat hackers and similar. Security technicians need to harness the power of AI.
Ahmer Inam is the chief artificialintelligence officer (CAIO) at Pactera EDGE. machinelearning and simulation). Ahmer Inam. Contributor. Share on Twitter. He has more than 20 years of experience driving organizational transformation. His experience includes leadership roles at Nike Inc.,
Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machinelearning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.
Artificialintelligence has moved from the research laboratory to the forefront of user interactions over the past two years. From fostering an over-reliance on hallucinations produced by knowledge-poor bots, to enabling new cybersecurity threats, AI can create significant problems if not implemented carefully and effectively.
Jeff Schumacher, CEO of artificialintelligence (AI) software company NAX Group, told the World Economic Forum : “To truly realize the promise of AI, businesses must not only adopt it, but also operationalize it.” He is reachable through his website: mtwriting.com.
Artificialintelligence (AI) has long been a cornerstone of cybersecurity. From malware detection to network traffic analysis, predictive machinelearning models and other narrow AI applications have been used in cybersecurity for decades.
Most artificialintelligence models are trained through supervised learning, meaning that humans must label raw data. Data labeling is a critical part of automating artificialintelligence and machinelearning model, but at the same time, it can be time-consuming and tedious work.
Hence, it is one of the vast industries of India that can be suitable to build a secure career path. hence, if you want to interpret and analyze big data using a fundamental understanding of machinelearning and data structure. AI or ArtificialIntelligence Engineer. Cybersecurity Specialist. Conclusion.
LOVO , the Berkeley, California-based artificialintelligence (AI) voice & synthetic speech tool developer, this week closed a $4.5 The proceeds will be used to propel its research and development in artificialintelligence and synthetic speech and grow the team. “We The Global TTS market is projected to increase $5.61
Sovereign AI refers to a national or regional effort to develop and control artificialintelligence (AI) systems, independent of the large non-EU foreign private tech platforms that currently dominate the field. This ensures data privacy, security, and compliance with national laws, particularly concerning sensitive information.
Digital transformation started creating a digital presence of everything we do in our lives, and artificialintelligence (AI) and machinelearning (ML) advancements in the past decade dramatically altered the data landscape. Cybersecurity underwent a similar evolution over the past 20 years.
From artificialintelligence to blockchain and smart cities, the UAEs tech landscape is set to host some of the most significant gatherings of innovators, investors, and entrepreneurs in the region. The week will feature discussions on a range of tech topics, including fintech, digital transformation, smart cities, and cybersecurity.
At the heart of this shift are AI (ArtificialIntelligence), ML (MachineLearning), IoT, and other cloud-based technologies. The intelligence generated via MachineLearning. There are also significant cost savings linked with artificialintelligence in health care.
INE Security , a global leader in cybersecurity training and certifications, recognizes this as a critical issue and is leading an initiative for change by working with SMBs to bridge the IT/IS skills gap and bolster proactive cybersecurity measures. “The
“And when we work with other internal teams, we focus on evaluating risk tolerance, managing quality outcomes, and securing our perimeter, all with a collaborative spirit.” Bringing together that collaborative spirit, innovative mindset, and technology expertise has created some real wins for Peoples and his team.
The already heavy burden born by enterprise security leaders is being dramatically worsened by AI, machinelearning, and generative AI (genAI). Informationsecurity leaders need an approach that is comprehensive, flexible and realistic. Enterprise security leaders can start by focusing on a few key priorities.
Take cybersecurity, for example. A staggering 21% of organizations report a severe shortage of skilled cybersecurity professionals, with another 30% finding it difficult to find suitable candidates. Only 8% of organizations have a relatively easy time finding qualified cybersecurity experts.
However, today’s startups need to reconsider the MVP model as artificialintelligence (AI) and machinelearning (ML) become ubiquitous in tech products and the market grows increasingly conscious of the ethical implications of AI augmenting or replacing humans in the decision-making process.
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