Artificial Intelligence
Zero Trust: SANS Unveils Critical Challenges and Solutions
As organizations continue to fortify their cybersecurity strategies in response to an ever-evolving threat landscape, many are turning to Zero Trust architectures to safeguard their data. However, implementing Zero Trust is not without its challenges. According to a new strategy guide from the SANS Institute, “Navigating the Path to a State of Zero Trust in 2024,” businesses often stumble over key obstacles in their journey towards Zero Trust adoption.
“The path to achieving a true state of Zero Trust isn’t straightforward. Organizations often encounter several fundamental challenges when attempting to implement end-to-end Zero Trust principles across their environment,” said Ismael Valenzuela, SANS Senior Instructor and author of the Cyber Defense and Blue Team Operations course, SANS SEC530: Defensible Security Architecture and Engineering. “By understanding and addressing these common mistakes, businesses can make better strategic and tactical decisions and increase their resiliency in the face of evolving threats.”
SANS Institute identified the top five mistakes made when implementing Zero Trust:
- Overlooking the Importance of Organizational Culture: Zero Trust is more than just a technological shift; it requires a fundamental change in organizational culture. Chief Information Security Officers (CISOs) must align security with strategic, operational, and financial priorities. As the strategy guide states, “Effective security is driven by people, processes, and technology.” Failure to secure stakeholder buy-in from the outset can doom Zero Trust initiatives to fail.
- Underestimating Human Risk: Employee error and negligence account for over 80% of data breaches. Hybrid work environments blur the lines between personal and professional spaces, increasing the complexity of monitoring user activity. “A Zero Trust architecture is an important line of defence against human risk,” the strategy guide emphasizes. Organizations must implement continuous monitoring and real-time assessment of user behaviour to mitigate these risks.
- Neglecting the Supply Chain: Recent high-profile supply chain attacks have underscored the vulnerabilities within interconnected systems. According to Gartner, by 2025, 45% of organizations worldwide will have experienced attacks on their supply chains. Zero Trust principles help limit the impact of these breaches by ensuring continuous verification and deeper visibility into user activity.
- Failing to Plan for Sustainable Success: Implementing Zero Trust is a long-term commitment that requires continuous improvement and adaptation. The SANS strategy guide highlights the importance of effective change management practices: “Effective change management ensures stakeholder buy-in, facilitates user adoption, minimizes disruption, promotes continuous improvement, and enhances collaboration.”
- Inadequate Measurement of Success: Measuring the effectiveness of a Zero Trust framework is crucial for maintaining stakeholder support. The guide suggests several metrics, including authentication success rates, policy compliance rates, and the time to detect and respond to incidents. These metrics provide a clear picture of the framework’s impact and highlight areas for improvement.
“Adopting the Zero Trust ‘never trust, always verify’ mindset is essential for modern cybersecurity,” said Valenzuela. “However, the real challenge lies in having a realistic understanding of what a Zero Trust architecture looks like and avoiding common pitfalls during implementation. From cultural shifts to technical deployments, this offers vital guidance to help organizations successfully navigate the complexities of Zero Trust and enhance their cybersecurity resilience.”
Artificial Intelligence
Check Point Leverages AI to Strengthen Network Security
Check Point Software Technologies has announced the new Check Point Quantum Firewall Software R82 (R82) and additional innovations for the Infinity Platform. As organizations face a 75% surge in cyber-attacks worldwide, R82 delivers new AI-powered engines to prevent zero-day threats including phishing, malware, and domain name system (DNS) exploits. It also includes new architectural changes and innovations that drive DevOps agility for data centre operations as well as simplicity and scale.
“Threats are continuing to multiply exponentially, and organizations need intelligent solutions that can keep them a step ahead,” said Nataly Kremer, Chief Product Officer at Check Point Software Technologies. “Network security is increasingly strategic. Our suite of AI-powered threat prevention tools – from Check Point Quantum Firewall Software R82 to GenAI Protect and more – are not only bringing world-class innovations but also relentlessly focused on making it operationally simple and resilient.”
Quantum Software R82 delivers over 50 new capabilities for enterprise customers including:
- Industry Leading AI-Powered Threat Prevention to block 99.8% of zero-day threats. It introduces four new AI engines to find hidden relationships and patterns to block over 500K additional attacks per month that protect against sophisticated zero-day phishing and malware campaigns.
- Agile Datacenter Operations to accelerate app development with automated integration of security policy.
- With dramatically simplified firewall virtualization, organizations achieve 3X faster provisioning of virtual systems for multi-tenancy and agile application development benefiting DevOps.
- Operational Simplicity to offer seamless scalability for networks of all sizes, automatically adapting to business growth and traffic spikes. It enables organizations to achieve resilience with built-in load sharing and clustering technology (ElasticXL) while benefiting from 3x faster provisioning and operations for firewall management.
- Post-Quantum Cryptography (PQC) to provide the latest NIST-approved cryptography Kyber (ML-KEM) for quantum-safe encryption, assuring that today’s encrypted data won’t turn into tomorrow’s treasure chest for threat actors.
“Maintaining effective network security requires AI, automation, and the ability to adapt quickly to the latest threats,” said Frank Dickson, IDC Group Vice President of Security and Trust. “Security needs to be strong, but it also needs to enable business innovation at the speed of DevOps. With Check Point’s new collaborative AI-powered solutions and Quantum Firewall Software, Check Point looks to deliver high-performance AI threat prevention while enabling organizations to innovate quickly.”
The new capabilities build upon Check Point’s recently released suite of AI-powered threat prevention innovations:
- Check Point Infinity AI Copilot is a responsive AI-powered assistant designed to automate and accelerate security management and threat resolution.
- Check Point GenAI Protect is a pioneering solution for the safe adoption of generative AI in enterprises.
- Check Point Infinity External Risk Management (ERM) delivers continuous monitoring and real-time threat prevention, augmented by expert-managed services. This protects customers against a wider array of external risks, from credential threat and vulnerability exploitation to phishing attacks and fraud.
“We’ve seen a definite performance increase and operational value with our upgrade to Check Point’s Quantum Firewall Software R82 software release. The new Quantum Firewall software allows us to secure and manage our encrypted traffic more easily than ever,” said Jeff Burgess, Manager of I.T. Enterprise, Aviation Technical Services. “With Check Point, all of our security products are working in sync together to provide a level of security which was previously unattainable.”
Artificial Intelligence
Dataiku Launches LLM Guard Services to Control Generative AI Rollouts
Dataiku has announced the launch of its LLM Guard Services suite, designed to advance enterprise GenAI deployments at scale from proof-of-concept to full production without compromising cost, quality, or safety. Dataiku LLM Guard Services includes three solutions: Cost Guard, Safe Guard, and the newest addition, Quality Guard. These components are integrated within the Dataiku LLM Mesh, the market’s most comprehensive and agnostic LLM gateway, for building and managing enterprise-grade GenAI applications that will remain effective and relevant over time. LLM Guard Services provides a scalable no-code framework to foster greater transparency, inclusive collaboration, and trust in GenAI projects between teams across companies.
Today’s enterprise leaders want to use fewer tools to reduce the burden of scaling projects with siloed systems, but 88% do not have specific applications or processes for managing LLMs, according to a recent Dataiku survey. Available as a fully integrated suite within the Dataiku Universal AI Platform, LLM Guard Services is designed to address this challenge and mitigate common risks when building, deploying, and managing GenAI in the enterprise.
“As the AI hype cycle follows its course, the excitement of two years ago has given way to frustration bordering on disillusionment today. However, the issue is not the abilities of GenAI, but its reliability,” said Florian Douetteau, Dataiku CEO. “Ensuring that GenAI applications deliver consistent performance in terms of cost, quality, and safety is essential for the technology to deliver its full potential in the enterprise. As part of the Dataiku Universal AI platform, LLM Guard Services is effective in managing GenAI rollouts end-to-end from a centralized place that helps avoid costly setbacks and the proliferation of unsanctioned ‘shadow AI’ – which are as important to the C-suite as they are for IT and data teams.”
Dataiku LLM Guard Services provides oversight and assurance for LLM selection and usage in the enterprise, consisting of three primary pillars:
- Cost Guard: A dedicated cost-monitoring solution to enable effective tracing and monitoring of enterprise LLM usage to anticipate better and manage spend vs. budget of GenAI.
- Safe Guard: A solution that evaluates requests and responses for sensitive information and secures LLM usage with customizable tooling to avoid data abuse and leakage.
- Quality Guard: The newest addition to the suite that provides quality assurance via automatic, standardized, code-free evaluation of LLMs for each use-case to maximize response quality and bring both objectivity and scalability to the evaluation cycle.
Previously, companies deploying GenAI have been forced to use custom code-based approaches to LLM evaluation or leverage separate, pure-play point solutions. Now, within the Dataiku Universal AI Platform, enterprises can quickly and easily determine GenAI quality and integrate this critical step in the GenAI use-case building cycle. By using LLM Quality Guard, customers can automatically compute standard LLM evaluation metrics, including LLM-as-a-judge techniques like answer relevancy, answer correctness, context precision, etc., as well as statistical techniques such as BERT, Rouge and Bleu, and more to ensure they select the most relevant LLM and approach to sustain GenAI reliability over time with greater predictability. Further, Quality Guard democratizes GenAI applications so any stakeholder can understand the move from proof-of-concept experiments to enterprise-grade applications with a consistent methodology for evaluating quality.
Artificial Intelligence
Cloudflare Helps Content Creators Regain Control of their Content from AI Bots
Cloudflare has announced AI Audit, a set of tools to help websites of any size analyse and control how their content is used by artificial intelligence (AI) models. For the first time, website and content creators will be able to quickly and easily understand how AI model providers are using their content, and then take control of whether and how the models can access it. Additionally, Cloudflare is developing a new feature where content creators can reliably set a fair price for their content that is used by AI companies for model training and retrieval augmented generation (RAG).
Website owners, whether for-profit companies, media and news publications, or small personal sites, may be surprised to learn AI bots of all types are scanning their content thousands of times every day without the content creator knowing or being compensated, causing significant destruction of value for businesses large and small. Even when website owners are aware of how AI bots are using their content, they lack a sophisticated way to determine what scanning to allow and a simple way to take action. For society to continue to benefit from the depth and diversity of content on the Internet, content creators need the tools to take back control.
“AI will dramatically change content online, and we must all decide together what its future will look like,” said Matthew Prince, co-founder and CEO, Cloudflare. “Content creators and website owners of all sizes deserve to own and have control over their content. If they don’t, the quality of online information will deteriorate or be locked exclusively behind paywalls. With Cloudflare’s scale and global infrastructure, we believe we can provide the tools and set the standards to give websites, publishers, and content creators control and fair compensation for their contribution to the Internet, while still enabling AI model providers to innovate.”
With AI Audit, Cloudflare aims to give content creators information and take back control so there can be a transparent exchange between the websites that want greater control over their content, and the AI model providers that are in need of fresh data sources so that everyone benefits. With this announcement, Cloudflare aims to help any website:
- Automatically control AI bots, for free: AI is a quickly evolving space, and many website owners need time to understand and analyze how AI bots are affecting their traffic or business. Many small sites don’t have the skills or bandwidth to manually block AI bots. The ability to block all AI bots in one click puts content creators back in control.
- Tap into analytics to see how AI bots access their content: Every site using Cloudflare now has access to analytics to understand why, when, and how often AI models access their website. Website owners can now make a distinction between bots – for example, text-generative bots that still credit the source of the data they use when generating a response, versus bots that scrape data with no attribution or credit.
- Better protect their rights when negotiating with model providers: An increasing number of sites are signing agreements directly with model providers to license the training and retrieval of content in exchange for payment. Cloudflare’s AI Audit tab will provide advanced analytics to understand metrics that are commonly used in these negotiations, like the rate of crawling for certain sections or the entire page. Cloudflare will also model terms of use that every content creator can add to their sites to legally protect their rights.
- Set a fair price for the right to scan content and transact seamlessly (in development): Many site owners, whether they are the large companies of the future or a high-quality individual blogs, do not have the resources, context, or expertise to negotiate one-off deals that larger publishers are signing with AI model providers, and AI model providers do not have the bandwidth to do this with every site that approaches them. In the future, even the largest content creators will benefit from Cloudflare’s seamless price setting and transaction flow, making it easy for model providers to find fresh content to scan they may otherwise be blocked from, and content providers to take control and be paid for the value they create.
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