Author: maribellopez

With the average organization using dozens of cybersecurity tools, security and IT leaders are drowning in complexity and expense. According to Palo Alto Networks’ research, the average organization faces nearly 2 million security-related findings, making it impossible for security teams to prioritize and address all potential threats effectively.

Adding to this complexity, cloud infrastructure changes rapidly, creating an ever-evolving risk landscape. The company’s research shows that 45% of cloud infrastructure risks change monthly. Meanwhile, adversaries are using AI to deliver more effective attacks more efficiently, cutting their time to data theft in half over recent months. While attackers may benefit from new AI capabilities, new AI-infused security solutions will also benefit defenders.

Market research firms, such as Gartner and IDC, have predicted the convergence of cloud security and traditional security operations as a key trend that will minimize these challenges. For example, IDC’s “FutureScape: Worldwide Security and Trust 2024 Predictions” projected that by 2026, 60% of enterprises will consolidate their cloud security tools into unified platforms that integrate with security operations. Meanwhile, companies shared with Lopez Research that they are looking for AI-powered solutions to minimize alert fatigue and provide intelligent remediation recommendations based on correlating data across multiple products.

Cortex Cloud Aims to Improve and Simplify Security

Palo Alto Networks took this challenge head-on with the announcement of its Cortex Cloud platform. Cortex Cloud integrates and evolves its Prisma Cloud capabilities. This shift represents more than a simple rebranding for Palo Alto because it’s a fundamental re-architecture of the platform that aims to unify cloud security with security operations center (SOC) capabilities.

This platform integration enables security teams to see the complete picture of their security landscape, from application code to cloud infrastructure to runtime environments. Cortex Cloud also integrates with third-party security tools and scanners to preserve existing security investments while benefiting from unified analytics and automation. Specifically, the Cortex Cloud platform offers four components that enable companies to:

• Minimize application security vulnerabilities. Cortex Cloud identifies and prioritizes issues across the development pipeline with end-to-end context across code, runtime, cloud and third-party scanners. This part of the solution supports preventing issues in app development before they become production issues that attackers can target.
• Create unified cloud posture visibility. Cortex Cloud builds on Prisma Cloud’s capabilities. It unifies visibility in one natively integrated platform, including cloud security posture management (CSPM), cloud infrastructure entitlement management (CIEM), data security posture management (DSPM), AI security posture management (AI-SPM), compliance and vulnerability management (CWP). It also supports AI-driven prioritization and automation-first remediation of multi-cloud risks.
• Update Cloud runtime features to stop attacks in real-time. Cortex Cloud natively integrates the unified Cortex XDR agent, enriched with additional cloud data sources, to prevent threats with advanced analytics.
• Deliver AI-driven SOC transformation. Cortex Cloud natively integrates cloud data, context and workflows within Cortex XSIAM to significantly reduce the mean time required to respond (MTTR) to modern threats with a single, unified Security Operations (SecOps) solution.

How Does Cortex Cloud Benefit Buyers?

The platform’s unified approach brings several practical benefits, such as unified dashboards and reporting across all security functions. At the heart of Cortex Cloud is its unified data plane, which integrates data from various sources, including cloud posture, runtime and application security. When security incidents occur, teams no longer need to manually coordinate between different departments and tools — all relevant information is available in one place, with unified reporting and consistent role-based access controls. However, Cortex Cloud goes beyond simply identifying security issues; it provides rich contextual information to help security teams understand the full scope and impact of security incidents.

Cortex Cloud leverages advanced analytics and machine learning to prioritize alerts and security threat findings intelligently. The platform helps security teams focus on the most pressing issues that require immediate attention by considering factors such as threat intelligence, asset criticality and risk profiles. This AI-powered approach significantly reduces the time and effort needed to identify and address potential security breaches, enabling organizations to respond more effectively to evolving threats. This contextual awareness is particularly valuable in complex, multi-layered cloud environments where the relationships between different components can be challenging to discern.

One of the other key benefits of Cortex Cloud is its automation capabilities. The platform can automatically remediate specific security issues, such as misconfigurations, without manual intervention. This feature reduces the workload on security teams and ensures that potential vulnerabilities are addressed promptly, minimizing the risk of exploitation. By automating routine tasks, Cortex Cloud empowers security professionals to focus on more strategic initiatives and proactive threat hunting.

Meeting Customers Where They Are

Palo Alto Networks’ move can potentially disrupt existing market prices by offering all multiple capabilities in a single package rather than requiring separate purchases for different controls. In one case, Palo Alto Networks demonstrated how its pricing would compare with that of one of its competitors using publicly available rates on the AWS Marketplace. In that scenario, the Cortex Cloud pricing was approximately 50% less than competitive solutions while providing more comprehensive coverage.

But this doesn’t translate into a one-size-fits-all buying approach for customers. For existing Prisma Cloud customers, Palo Alto Networks offers flexible migration options, including like-for-like upgrades at no additional cost. The company emphasizes that adding the new Cloud Runtime Security or SOC capabilities is optional, and buyers can work with channel partners and their sales representatives to select what’s right for the organization.

Accelerating the Industry Shift

The announcement represents a significant shift in how enterprise security and cloud teams could achieve a more holistic security approach. Rather than treating cloud security as a separate domain, organizations can now manage it as part of their broader security operations strategy with shared intelligence, unified workflows and automated responses. For organizations struggling with alert fatigue and siloed security tools, Palo Alto Networks’ integrated approach could provide a more manageable and effective way to secure their cloud environments.

There’s a significant opportunity for Palo Alto to gain a larger share of wallet as companies move from fragmented point solutions to more comprehensive security platforms such as Cortex Cloud. However, the platform transition also presents distinct challenges. Organizations that have recently invested in various security products will understandably be hesitant to abandon these investments before realizing their full value. Additionally, enterprises must carefully weigh the benefits of platform consolidation against the potential risks of becoming overly dependent on a single vendor’s ecosystem. Overall, the shift towards leveraging AI and creating platforms represents a leap forward in simplifying and improving an organization’s ability to prevent cybersecurity threats.

How generative AI changes the quick service restaurant industry

Every industry, including the quick service restaurant (QSR) market, plans to transform its business with artificial intelligence (AI), especially generative AI. Several years ago, Wendy’s embarked on its AI journey, leveraging cloud computing services and generative AI to enhance employee and customer experiences. The drive-thru experience presents numerous challenges for QSR restaurants due to the complexities of menu options, limited-time offers, special requests, and ambient noise.

Wendy’s chose to tackle the drive-thru experience with AI because 75 to 80 percent of Wendy’s customers choose the drive-thru as their preferred ordering channel. The company saw a tremendous opportunity to improve the customer experience by creating a seamless ordering experience using AI automation in the drive-thru.

In an interview with Maribel Lopez of Lopez Research, Wendy’s CIO Matt Spessard shared how its AI program had advanced over the past year and shared advice for other leaders looking to tackle AI within their business. In 2024, Wendy’s announced an expansion of its partnership with Google Cloud, tapping into Google’s AI technology and resources to enhance its generative AI platform called Wendy’s FreshAI. Wendy’s Fresh AI aims to address challenges that traditional AI couldn’t solve, such as understanding casual conversations and handling the extensive customizations of Wendy’s menu. For example, the back-and-forth nature of conversations is an intensely complicated technical challenge for AI. It also added Spanish as a language option in 2024.

Traditional rule-based AI chatbots weren’t the answer because they can’t easily support natural conversations’ diverse and dynamic nature. For example, Wendy’s realized there are over 200 billion combinations of words and options to order a Dave’s Double. Additionally, it can take years of development and tremendous work to maintain, modify, and expand capabilities within these more rigid rules-based solutions. Today, Wendy’s uses generative AI to interpret conversations, create responses, and adapt in real-time instead of following a narrow set of rules.

While many AI discussions focus on job loss issues, Wendy’s shared that its AI efforts also benefit its employees. Wendy’s FreshAI works alongside restaurant teams, eliminating ordering issues while empowering crew members to focus on preparing and completing orders efficiently.

How does Wendy’s measure AI success and return?

Wendy’s takes a pragmatic view of what AI success means. Its efforts focus on delivering speed, accuracy, and consistency to customers. It measures these results with metrics such as how many orders were submitted without human intervention and the consistency of the customer experience at the drive-thru. What have been the results thus far? The percentage of orders successfully handled by Wendy’s FreshAI without restaurant team member intervention averaged 86%, and it expects the average to increase.

One test site showed service times 22 seconds faster than the Columbus, Ohio market average. Wendy’s noted that other QSR companies define “accuracy” as any order started by the AI assistant and submitted to the point-of-sale system, including orders where a crew member joins the conversation to correct an inaccuracy. Wendy’s shared that if it uses that broader definition of accuracy, its FreshAI success rate reaches nearly 99%.

What’s next for AI at Wendy’s

Wendy’s FreshAI has moved from pilot to production. It is now available in nearly 100 restaurants across 17 states with Spanish language capabilities. There’s still tremendous upside to expanding the technology deployment as Wendy’s operates over 7,000 restaurants globally. Going forward, Wendy’s sees the Fresh AI assistant as a platform that will scale across various ordering channels, such as mobile apps, kiosks, and smart devices.

Advice on tackling AI innovation?

Wendy’s CIO Spessard said the company learned many lessons along the way. He provided the following key pieces of advice for organizations looking to adopt AI:

1. Identify the right use case and technology. Wendy’s emphasizes that organizations need to think pragmatically about the use cases where AI can provide the most value and then select the appropriate AI technology (e.g., generative AI, traditional machine learning) to fit that specific need. AI is a broad spectrum of technologies, so the “right tool for the right job” approach is essential.
2. Start with small-scale experiments. Spessard recommends that companies start with small-scale experiments within the organization, exposing employees to AI capabilities. This exposure helps organically grow the usage of AI as more use cases are identified. Spessard said Wendy’s had to resist the urge to deploy too widely and quickly. Iterating in the early stages of its AI deployment helped Wendy’s achieve its desired accuracy and consistency.
3. Emphasize continuous improvement. Wendy’s stressed the importance of a feedback loop, continuous iteration, and a mindset of even 1% improvement every day. It regularly analyzes customer and employee feedback, allowing Wendy’s to refine aspects like tone, cadence, and phrasing in Wendy’s FreshAI. These refinements were critical to enhancing the AI assistant’s performance.
4. Engage stakeholders early and often. Wendy’s found it very valuable to regularly perform demonstrations of the AI assistant’s capabilities and progress to key stakeholders like franchisees. These demos helped build trust and support for the initiative as stakeholders could see the improvements over time. Additionally, Wendy’s noted that the rapid democratization of AI technologies is making change management easier, as employees are already familiar with using AI in their personal lives, setting the expectation for it in the workplace as well.

A key takeaway from the discussion was to ground AI innovation in iterative improvement, continuously engage employees and partners in the process, and cultivate a willingness to experiment and learn rather than trying to perfect the technology before deployment.

Last week, SAP announced a major evolution in its enterprise data management platform with the launch of SAP Business Data Cloud. SAP Business Data Cloud represents a significant advancement in how enterprises can manage, analyze, and derive value from their data. As Irfan Khan, Chief Product Officer and President of Data and Analytics at SAP, explained in an interview with Maribel Lopez of Lopez Research: “Business data cloud takes all of SAP core business applications and shares the data in a zero copy fashion with whomever the consumer may be.”

The Databricks Partnership

One of the most significant aspects of this announcement is the deepened partnership with Databricks. The integration brings Databricks’ capabilities directly into SAP Business Data Cloud, creating a powerful combination for enterprises looking to leverage AI and machine learning. As Khan noted, “SAP Databricks now is part of the building materials of our business data cloud,” which he explains will radically improve time to market and value for customers.

Addressing Key Industry Challenges

The announcement comes at a crucial time, as organizations continue to struggle with data quality and integration. According to SAP’s global survey of 1,200 business and technology leaders, 55 percent cite poor data quality as their biggest challenge, with nearly half struggling to harmonize data across ecosystems.

At the SAP analyst innovation summit, companies using the early preview of business data cloud shared under NDA their enthusiasm for the speed at which they could develop systems that leveraged a variety of business data and the insights that could be gained from turning this information into AI-led intelligence. Markus Hartmann, Corporate Vice President and Head of Business Technology at Henkel, echoed this perspective in the SAP blog when he stated: “SAP Business Data Cloud will help us unlock the value of our data and drive innovation across our business. Its semantically rich data products and deep Databricks integration will connect and enhance our existing data products, ensuring long-term adaptability.”

Looking Ahead

For organizations wondering where to begin, Khan offers clear guidance: start with Business Warehouse modernization. “Look no further than the most critical data that you have typically sit inside a BW (business warehouse) environment,” he advises, highlighting the opportunity to leverage existing data for AI and advanced analytics initiatives.

The platform is designed with openness and customer choice in mind, featuring integrations with partners like Collibra, Confluent, and DataRobot, with more strategic partnerships on the horizon. This ecosystem approach, combined with the Databricks integration, positions SAP Business Data Cloud as a significant step forward in enterprise data management and AI enablement.