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Securing these technologies is paramount in a region where digital infrastructure is critical to national development. As the GCC countries push ahead with digital transformation, ensuring the security of 5G, AI, and IoT ecosystems has become more complex. But security must evolve with it.”
In its Guidelines and Companion Guide for Securing AI Systems, Singapore’s Cyber Security Agency (CSA) stressed that AI systems must be secure by design and secure by default, like other digital systems.
The main commercial model, from OpenAI, was quicker and easier to deploy and more accurate right out of the box, but the open source alternatives offered security, flexibility, lower costs, and, with additional training, even better accuracy. Finally, in addition to security and flexibility, cost is a key factor.
As organizations look to modernize IT systems, including the mainframe, there’s a critical need to do so without sacrificing security or falling out of compliance. With the stakes so high, IT leaders need to ensure their modernization strategies are inclusive of mainframe security. PCI DSS v4.0).
Securities and Exchange Commission (SEC) Climate disclosure rules The European Commission’s Green Claims Directive California’s passing of SB 253 and SB 261 The impact of Capricorn season signaling on sustainability and social impact communication is also unmistakable, particularly as it relates to public and private reporting.
However, the diversity and velocity of data utilized by AI pose significant challenges for data security and compliance. Without proper data governance, transparency, and security, customer data, intellectual property, or other sensitive corporate information can be fed into LLM models, risking unintended data leakage.
A member of your organization’s security team reads about a new kind of security tool and brings it to the CISO’s attention, who decides that it’s a good investment. The CISO sees a new kind of security threat that requires a different security tool. A colleague recommends a security tool she says is indispensable.
In today’s enterprise environments, security and networking teams may be siloed for a variety of reasons. Yet there’s now widespread agreement that the drawbacks of siloed security and networking operations far outweigh any historical advantages. Plus, enhanced security outcomes lead to impressive reputational dividends.
Today, security teams worldwide are under immense pressure. Today’s cybercriminals are leveraging advanced techniques to breach security perimeters – ransomware attacks are more targeted, phishing campaigns are increasingly sophisticated, and attackers are exploiting new vulnerabilities.
The security professional shortage Some 3.5 This drives up wages, making it both expensive to hire security professionals and difficult to retain them. Given the nature of their business, costs for security are baked into the business model. But you need to know what to look for in a cloud provider.
The already heavy burden born by enterprise security leaders is being dramatically worsened by AI, machine learning, and generative AI (genAI). Information security leaders need an approach that is comprehensive, flexible and realistic. Adopting still more, individual security tools, now with AI incorporated, is already happening.
One key reason is that CIOs and chief information security officers (CISOs) are not always aligned in how to enable productive, secure work. But in practice, IT and security teams often work separately, without common knowledge, data, goals, priorities, and practices. Ultimately you’re impacting top line revenue,” says Fulton.
New security and risk solutions will be necessary as AI agents significantly increase the already invisible attack surface at enterprises. Guardian Agents’ build on the notions of security monitoring, observability, compliance assurance, ethics, data filtering, log reviews and a host of other mechanisms of AI agents,” Gartner stated. “In
AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. Key challenges include designing and deploying AI infrastructure, with priorities such as data security (53%), resilience and uptime (52%), management at scale (51%), and automation (50%).
Unpredictable weather patterns, extreme temperature fluctuations, and shifting seasons threaten crop yields and food security. SupPlant’s use of AI and real-time data, powered by Astra DB, is helping farmers worldwide combat the effects of climate change and improve food security.
Prepare for the pending quantum threat Heading into 2025, CIOs should prepare their systems and data for the upcoming quantum computing threat , warns Ted Shorter, CTO of security technology provider Keyfactor. The pace of change in the global market and technology landscape demands organizations that can adapt quickly.
While poised to fortify the security posture of organizations, it has also changed the nature of cyberattacks. From prompt injections to poisoning training data, these critical vulnerabilities are ripe for exploitation, potentially leading to increased security risks for businesses deploying GenAI.
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.
Deepak Jain, CEO of a Maryland-based IT services firm, has been indicted for fraud and making false statements after allegedly falsifying a Tier 4 data center certification to secure a $10.7 million contract with the US Securities and Exchange Commission (SEC). Queries to AiNET, however, did not elicit any response.
For Du, this investment in Oracle’s sovereign cloud infrastructure is a strategic move to ensure that the UAE’s public sector embraces AI and cloud services within a framework that upholds data sovereignty and national security. Du has made it clear that security is their top priority, particularly when dealing with government data.
As concerns about AI security, risk, and compliance continue to escalate, practical solutions remain elusive. Thus, CISOs must emphasize the need for a balance between accessibility and security and oversee the growing demand for logging and tracking capabilities. training image recognition models to misidentify objects).
The premise was that enterprises needed to secure their critical data more stringently in the wake of data hacks and emerging AI processes. I wrote, “ It may be even more important for the security team to protect and maintain the integrity of proprietary data to generate true, long-term enterprise value.
I am excited about the potential of generative AI, particularly in the security space, she says. The opportunity to further leverage AI to enhance our security infrastructure, address threats, and enable fraud detection is immense, she says. One of them is Katherine Wetmur, CIO for cyber, data, risk, and resilience at Morgan Stanley.
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.
Security weaknesses arise Security and risk vulnerabilities are important signs that modernization is immediately necessary. He notes that recent surveys by Gartner and Forrester show that over 50% of organizations cite security and efficiency as their main reasons for modernizing their legacy systems and data applications.
This award-winning access management project uses automation to streamline access requests and curb security risks. Altogether, these automation tools have improved both security and efficiency,” he says. This has saved a lot of time while also making the interactions more secure.”
NTT believes that AI should respect human rights and diversity; that it be fair, unbiased and transparent; that it protects personal data; that it be secure; and that it will not only create new business opportunities, but also benefit people and the planet. NTT has its own internal set of principles that guide its approach to AI.
Ensure security and access controls. Modern data architectures must be designed for security, and they must support data policies and access controls directly on the raw data, not in a web of downstream data stores and applications. Ensure data governance and compliance. Choose the right tools and technologies. Scalable data pipelines.
This creates new risks around data privacy, security, and consistency, making it harder for CIOs to maintain control. And the middle contains the trust, risk, and security management (TRiSM) technologies that make it all safe.” To navigate this, Gartner has advocated for a layered approach, describing it as a “tech sandwich.”
Cybersecurity company Camelot Secure, which specializes in helping organizations comply with CMMC, has seen the burdens of “compliance overload” first-hand through its customers. But for now, Myrddin’s specialty is helping organizations comply with CMMC quickly and securely through automation.
Ensuring effective and secure AI implementations demands continuous adaptation and investment in robust, scalable data infrastructures. They support the integration of diverse data sources and formats, creating a cohesive and efficient framework for data operations.
The complexity of IT and security infrastructure was highlighted as the greatest obstacle to achieving cyber resilience according to new research, Unlock the Resilience Factor from Zscaler. Expectation of disruption Limited investment in cyber resilience remains a challenge, despite rising security budgets overall: nearly 49% of U.S.-based
In addition, because they require access to multiple data sources, there are data integration hurdles and added complexities of ensuring security and compliance. Sharpening security and compliance So what happens if a human orders the agentic system to do something he or she doesnt have a right to? The information is pushed to them.
Here are some excellent use cases for genAI: Hardening security: Security professionals must deal with so much data and so many alerts that important threat indicators can get lost in the noise. That means that admins can spend more time addressing and preventing threats and less time trying to interpret security data and alerts.
In some cases, that may be a better alternative than moving mission-critical data to other hardware, which may not be as secure or resilient, she adds. Many organizations have their mission-critical data residing on mainframes, and it may make sense to run AI models where that data resides, Dyer says.
Much of this data must adhere to regulations for organizations to remain compliant, which is why they are often housed in a secure mainframe. Ensuring security and compliance during data transit Mainframes are some of the most secure environments in IT, housing highly sensitive transactional data.
By exploring the use of confidential computing, Core42 and AMD aim to address the increasing demand for secure, compliant cloud infrastructures, especially in regions with strict data sovereignty regulations. The collaboration highlights the growing importance of sovereign cloud infrastructure and AI in the Middle East.
Maintaining, updating, and patching old systems is a complex challenge that increases the risk of operational downtime and security lapse. Speed: Does it deliver rapid, secure, pre-built tools and resources so developers can focus on quality outcomes for the business rather than risk and integration?
A hybrid approach often offers the best solution, allowing organizations to store and process sensitive information securely on-premises while leveraging the scalability and flexibility of the cloud for less critical workloads. It means managing and storing data where it will bring the most value to the enterprise, without having to move it.
Prioritize data quality and security. For AI models to succeed, they must be fed high-quality data thats accurate, up-to-date, secure, and complies with privacy regulations such as the Colorado Privacy Act, California Consumer Privacy Act, or General Data Protection Regulation (GDPR). That said, watch for data bias.
Intelligent data services With the rise of AI, there is an increasing need for robust security and governance to protect sensitive data and to comply with regulatory requirements, especially in the face of threats like ransomware. Discover our intelligent data services and innovations for secure, agile AI
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.
“We built this whole thing so that the models are swappable, and we’ll constantly be evaluating which models we use based on mostly price performance, but also just what the risk profile looks like to us,” says Beswick, whose team also built a security and governance platform to serve as the foundation for the firm’s AI development.
Microsofts Azure infrastructure and ecosystem of software tooling, including NVIDIA AI Enterprise, is tightly coupled with NVIDIA GPUs and networking to establish an AI-ready platform unmatched in performance, security, and resiliency.
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