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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.”
Meta will allow US government agencies and contractors in national security roles to use its Llama AI. The clarity on data sharing could be crucial, as it may impact how effectively the model adapts to government-specific needs while maintaining data security.
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.
As enterprises scale their digital transformation journeys, they face the dual challenge of managing vast, complex datasets while maintaining agility and security. Data masking for enhanced security and privacy Data masking has emerged as a critical pillar of modern data management strategies, addressing privacy and compliance concerns.
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.
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).
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.
CIOs should adopt a proactive, preventative approach managing enterprise applications holistically to prevent security gaps before they emerge. In many cases, companies should opt for closed, proprietary AI models that arent connected to the internet, ensuring that critical data remains secure within the enterprise.
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.
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.
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.
These areas are considerable issues, but what about data, security, culture, and addressing areas where past shortcuts are fast becoming todays liabilities? This will free them to bring their skills and creativity to higher-value activities such as enhancing data security and delivering innovative solutions for customers.
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%).
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.
Regardless of the driver of transformation, your companys culture, leadership, and operating practices must continuously improve to meet the demands of a globally competitive, faster-paced, and technology-enabled world with increasing security and other operational risks.
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).
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.
Analyst reaction to Thursday’s release by the US Department of Homeland Security (DHS) of a framework designed to ensure safe and secure deployment of AI in critical infrastructure is decidedly mixed. What if it goes rogue, what if it is uncontrolled, what if it becomes the next arms race, how will the national security be ensured?”
Broadcom and Google Clouds continued commitment to solving our customers most pressing challenges stems from our joint goal to enable every organizations ability to digitally transform through data-powered innovation with the highly secure and cyber-resilient infrastructure, platform, industry solutions and expertise.
He says even if no one can be 100% comfortable with the quality and quantity of the data fueling AI systems, they should feel confident that the quality and quantity are high enough for the use case, that the data is adequately secured, and that its use conforms to regulatory requirements and best practices such as those around privacy.
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.”
The requirements will vary, depending on whether the data to be labeled is proprietary or public, and whether there are any special considerations around privacy or security.
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.
It’s important to have security in use, and that the technology feels like a natural element,” he says. Of course, security was a priority before implementation, considering the amount of critical information handled on a daily basis. “That’s crucial for success.”
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
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.
On the contrary, poor planning and design decisions could result in a scenario where modernization spawns more cost, security and/or IT management problems than it solves. Protect data security and privacy Along similar lines, ensuring data security and privacy shouldnt be something you do after modernization is complete.
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?
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