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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.
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.
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.
In the Unit 42 Threat Frontier: Prepare for Emerging AI Risks report, we aim to strengthen your grasp of how generative AI (GenAI) is reshaping the cybersecurity landscape. The Evolving Threat Landscape GenAI is rapidly reshaping the cybersecurity landscape. Secure AI by design from the start.
In our eBook, Building Trustworthy AI with MLOps, we look at how machinelearning operations (MLOps) helps companies deliver machinelearning applications in production at scale. AI operations, including compliance, security, and governance. AI ethics, including privacy, bias and fairness, and explainability.
As Artificial Intelligence (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. The concern isnt that AI is making cybersecurity easier, said Wallace.
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. According to a Cloudera survey, 72% of business leaders agree that data governance is an enabler of business value, underscoring the critical link between secure data and impactful AI.
AI and machinelearning are poised to drive innovation across multiple sectors, particularly government, healthcare, and finance. Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance.
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.
Many organizations are dipping their toes into machinelearning and artificial intelligence (AI). MachineLearning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machinelearning lifecycle through automation and scalability.
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.
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.
As policymakers across the globe approach regulating artificial intelligence (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. See figure below.)
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.
Automation and machinelearning are augmenting human intelligence, tasks, jobs, and changing the systems that organizations need in order not just to compete, but to function effectively and securely in the modern world.
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.
The Austin, Texas-based startup has developed a platform that uses artificial intelligence 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
{{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.
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.
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.
These networks are not only blazing fast, but they are also adaptive, using machinelearning algorithms to continuously analyze network performance, predict traffic and optimize, so they can offer customers the best possible connectivity. This leaves them with significant complexity and security gaps.
Leveraging machinelearning and AI, the system can accurately predict, in many cases, customer issues and effectively routes cases to the right support agent, eliminating costly, time-consuming manual routing and reducing resolution time to one day, on average. I’ll give you one last example of how we use AI to fight fraud.
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.
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.
Job titles like data engineer, machinelearning engineer, and AI product manager have supplanted traditional software developers near the top of the heap as companies rush to adopt AI and cybersecurity professionals remain in high demand. An example of the new reality comes from Salesforce.
In the quest to reach the full potential of artificial intelligence (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.
It was truly a good use of time attending the 33rd RSA Conference in San Francisco, along with over 40,000 attendees, networking with the leading minds in the cybersecurity industry. Cybersecurity is a strategic battle, and a successful outcome depends on having the right knowledge and tools to stay ahead of attackers.
Like other data-rich industries, banking, capital markets, insurance and payments firms are lucrative targets with high-value information. Conversely, threat actors – from cybercriminals to nation-states – are harnessing AI to craft more sophisticated attacks, automate their operations, and evade traditional security measures.
As operational technology (OT) environments undergo rapid digital transformation, so do their security risks. We’re pleased to announce new advancements in our OT Security solution designed to address these evolving risks. These advancements ensure seamless security while minimizing the risk of disruption.
TRECIG, a cybersecurity and IT consulting firm, will spend more on IT in 2025 as it invests more in advanced technologies such as artificial intelligence, machinelearning, and cloud computing, says Roy Rucker Sr., Spending on advanced IT Some business and IT leaders say they also anticipate IT spending increases during 2025.
In 2024, cybersecurity has become a top priority for businesses across the globe. And with good reason – the cost of cybercrime globally now stands at over $8 trillion, with our proprietary research finding that an overwhelming 96% of companies experienced a cybersecurity incident in 2022.
Intro: Time was, a call center agent could be relatively secure in knowing who was at the other end of the line. And if they werent, multi-factor authentication (MFA), answers to security questions, and verbal passwords would solve the issue. Often, bots are involved in this process.
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.
The partnership is set to trial cutting-edge AI and machinelearning solutions while exploring confidential compute technology for cloud deployments. Core42 equips organizations across the UAE and beyond with the infrastructure they need to take advantage of exciting technologies like AI, MachineLearning, and predictive analytics.
Barely half of the Ivanti respondents say IT automates cybersecurity configurations, monitors application performance, or remotely checks for operating system updates. Yet the same report confirmed that DEX best practices are still not widely implemented in and by the IT team.
Other key uses include fraud detection, cybersecurity, and image/speech recognition. AI applications are evenly distributed across virtual machines and containers, showcasing their adaptability. AI applications are evenly distributed across virtual machines and containers, showcasing their adaptability.
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.
This alarming trend is a byproduct of the growing popularity of cloud computing and the “as-a-service” model, where services like infrastructure, recovery, and cybersecurity are now accessible on demand. This anonymity adds to the security of transactions, creating a low-risk, high-reward marketplace for would-be attackers.
Artificial intelligence (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.
As data is moved between environments, fed into ML models, or leveraged in advanced analytics, considerations around things like security and compliance are top of mind for many. In fact, among surveyed leaders, 74% identified security and compliance risks surrounding AI as one of the biggest barriers to adoption.
Artificial intelligence (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.
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. The bank has been dedicated to enhancing its digital platforms and improving customer experience.
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
Core principles of sovereign AI Strategic autonomy and security Countries, whether individually or collectively, want to develop AI systems that are not controlled by foreign entities, especially for critical infrastructure, national security, and economic stability. high-performance computing GPU), data centers, and energy.
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