AI Insights
AI-driven CDR: The shield against modern cloud threats
Cloud computing is the backbone of modern enterprise innovation, but with speed and scalability comes a growing storm of cyber threats. Cloud adoption continues to skyrocket. In fact, by 2028, cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives. The traditional perimeter has all but disappeared. The result? A significantly expanded attack surface and a growing volume of threats targeting cloud workloads.
Studies tell us that 80% of security exposures now originate in the cloud, and threats targeting cloud environments have recently increased by 66%, underscoring the urgency for security strategies purpose-built for this environment. The reality for organizations is stark. Legacy tools designed for static, on-premises architectures can’t keep up. What’s needed is a new approach—one that’s intelligent, automated, and cloud-native. Enter AI-driven cloud detection and response (CDR).
Why legacy tools fall short
Traditional security approaches leave organizations exposed. Posture management has been the foundation of cloud security, helping teams identify misconfigurations and enforce compliance. Security risks, however, don’t stop at misconfigurations or vulnerabilities.
- Limited visibility: Cloud assets are ephemeral, spinning up and down in seconds. Legacy tools lack the telemetry and agility to provide continuous, real-time visibility.
- Operational silos: Disconnected cloud and SOC operations create blind spots and slow incident response.
- Manual burden: Analysts are drowning in alerts. Manual triage can’t scale with the velocity and complexity of cloud-native threats.
- Delayed response: In today’s landscape, every second counts. 60% of organizations take longer than four days to resolve cloud security issues.
The AI-powered CDR advantage
AI-powered CDR solves these challenges by combining the speed of automation with the intelligence of machine learning—offering CISOs a modern, proactive defense. Organizations need more than static posture security. They need real-time prevention.
Real-time threat prevention detection: AI engines analyze vast volumes of telemetry in real time—logs, flow data, behavior analytics. The full context this provides enables the detection and prevention of threats as they unfold. Organizations with AI-enhanced detection reduced breach lifecycle times by more than 100 days.
Unified security operations: CDR solutions bridge the gap between cloud and SOC teams by centralizing detection and response across environments, which eliminates redundant tooling and fosters collaboration, both essential when dealing with fast-moving incidents.
Context-rich insights: Modern CDR solutions deliver actionable insights enriched with context—identifying not just the issue, but why the issue matters. It empowers teams to prioritize effectively, slashing false positives and accelerating triage.
Intelligent automation: From context enrichment to auto-containment of compromised workloads, AI-enabled automation reduces the manual load on analysts and improves response rates.
The path forward
Organizations face unprecedented pressure to secure fast-changing cloud environments without slowing innovation. Relying on outdated security stacks is no longer viable. Cortex Cloud CDR from Palo Alto Networks delivers the speed, context, and intelligence required to defend against the evolving threat landscape. With over 10,000 detectors and 2,600+ machine learning models, Cortex Cloud CDR identifies and prevents high-risk threats with precision.
It’s time to shift from reactive defense to proactive protection. AI-driven CDR isn’t just another tool—it’s the cornerstone of modern cloud security strategy. And for CISOs, it’s the shield your organization needs to stay resilient in the face of tomorrow’s threats.
AI Insights
Intro robotics students build AI-powered robot dogs from scratch
Equipped with a starter robot hardware kit and cutting-edge lessons in artificial intelligence, students in CS 123: A Hands-On Introduction to Building AI-Enabled Robots are mastering the full spectrum of robotics – from motor control to machine learning. Now in its third year, the course has students build and enhance an adorable quadruped robot, Pupper, programming it to walk, navigate, respond to human commands, and perform a specialized task that they showcase in their final presentations.
The course, which evolved from an independent study project led by Stanford’s robotics club, is now taught by Karen Liu, professor of computer science in the School of Engineering, in addition to Jie Tan from Google DeepMind and Stuart Bowers from Apple and Hands-On Robotics. Throughout the 10-week course, students delve into core robotics concepts, such as movement and motor control, while connecting them to advanced AI topics.
“We believe that the best way to help and inspire students to become robotics experts is to have them build a robot from scratch,” Liu said. “That’s why we use this specific quadruped design. It’s the perfect introductory platform for beginners to dive into robotics, yet powerful enough to support the development of cutting-edge AI algorithms.”
What makes the course especially approachable is its low barrier to entry – students need only basic programming skills to get started. From there, the students build up the knowledge and confidence to tackle complex robotics and AI challenges.
Robot creation goes mainstream
Pupper evolved from Doggo, built by the Stanford Student Robotics club to offer people a way to create and design a four-legged robot on a budget. When the team saw the cute quadruped’s potential to make robotics both approachable and fun, they pitched the idea to Bowers, hoping to turn their passion project into a hands-on course for future roboticists.
“We wanted students who were still early enough in their education to explore and experience what we felt like the future of AI robotics was going to be,” Bowers said.
This current version of Pupper is more powerful and refined than its predecessors. It’s also irresistibly adorable and easier than ever for students to build and interact with.
“We’ve come a long way in making the hardware better and more capable,” said Ankush Kundan Dhawan, one of the first students to take the Pupper course in the fall of 2021 before becoming its head teaching assistant. “What really stuck with me was the passion that instructors had to help students get hands-on with real robots. That kind of dedication is very powerful.”
Code come to life
Building a Pupper from a starter hardware kit blends different types of engineering, including electrical work, hardware construction, coding, and machine learning. Some students even produced custom parts for their final Pupper projects. The course pairs weekly lectures with hands-on labs. Lab titles like Wiggle Your Big Toe and Do What I Say keep things playful while building real skills.
CS 123 students ready to show off their Pupper’s tricks. | Harry Gregory
Over the initial five weeks, students are taught the basics of robotics, including how motors work and how robots can move. In the next phase of the course, students add a layer of sophistication with AI. Using neural networks to improve how the robot walks, sees, and responds to the environment, they get a glimpse of state-of-the-art robotics in action. Many students also use AI in other ways for their final projects.
“We want them to actually train a neural network and control it,” Bowers said. “We want to see this code come to life.”
By the end of the quarter this spring, students were ready for their capstone project, called the “Dog and Pony Show,” where guests from NVIDIA and Google were present. Six teams had Pupper perform creative tasks – including navigating a maze and fighting a (pretend) fire with a water pick – surrounded by the best minds in the industry.
“At this point, students know all the essential foundations – locomotion, computer vision, language – and they can start combining them and developing state-of-the-art physical intelligence on Pupper,” Liu said.
“This course gives them an overview of all the key pieces,” said Tan. “By the end of the quarter, the Pupper that each student team builds and programs from scratch mirrors the technology used by cutting-edge research labs and industry teams today.”
All ready for the robotics boom
The instructors believe the field of AI robotics is still gaining momentum, and they’ve made sure the course stays current by integrating new lessons and technology advances nearly every quarter.
This Pupper was mounted with a small water jet to put out a pretend fire. | Harry Gregory
Students have responded to the course with resounding enthusiasm and the instructors expect interest in robotics – at Stanford and in general – will continue to grow. They hope to be able to expand the course, and that the community they’ve fostered through CS 123 can contribute to this engaging and important discipline.
“The hope is that many CS 123 students will be inspired to become future innovators and leaders in this exciting, ever-changing field,” said Tan.
“We strongly believe that now is the time to make the integration of AI and robotics accessible to more students,” Bowers said. “And that effort starts here at Stanford and we hope to see it grow beyond campus, too.”
AI Insights
5 Ways CFOs Can Upskill Their Staff in AI to Stay Competitive
Chief financial officers are recognizing the need to upskill their workforce to ensure their teams can effectively harness artificial intelligence (AI).
AI Insights
Real or AI: Band confirms use of artificial intelligence for its music on Spotify
The Velvet Sundown, a four-person band, or so it seems, has garnered a lot of attention on Spotify. It started posting music on the platform in early June and has since released two full albums with a few more singles and another album coming soon. Naturally, listeners started to accuse the band of being an AI-generated project, which as it now turns out, is true.
The band or music project called The Velvet Sundown has over a million monthly listeners on Spotify. That’s an impressive debut considering their first album called “Floating on Echoes” hit the music streaming platform on June 4. Then, on June 19, their second album called “Dust and Silence” was added to the library. Next week, July 14, will mark the release of the third album called “Paper Sun Rebellion.” Since their debut, listeners have accused the band of being an AI-generated project and now, the owners of the project have updated the Spotify bio and called it a “synthetic music project guided by human creative direction, and composed, voiced, and visualized with the support of artificial intelligence.”
It goes on to state that this project challenges the boundaries of “authorship, identity, and the future of music itself in the age of AI.” The owners claim that the characters, stories, music, voices, and lyrics are “original creations generated with the assistance of artificial intelligence tools,” but it is unclear to what extent AI was involved in the development process.
The band art shows four individuals suggesting they are owners of the project, but the images are likely AI-generated as well. Interestingly, Andrew Frelon (pseudonym) claimed to be the owner of the AI band initially, but then confirmed that was untrue and that he pretended to run their Twitter because he wanted to insert an “extra layer of weird into this story,” of this AI band.
As it stands now, The Velvet Sundown’s music is available on Spotify with the new album releasing next week. Now, whether this unveiling causes a spike or a decline in monthly listeners, remains to be seen.
I have always been passionate about gaming and technology, which drove me towards pursuing a career in the tech writing industry. I have spent over 7 years in the tech space and about a decade in content writing. I hope to continue to use this passion and generate informative, entertaining, and accurate content for readers.
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