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IBM Framework for Securing Generative AI: Navigating the Future of Secure AI Workflows

In today’s rapidly evolving technological landscape, IBM is stepping up to the challenge of addressing the unique risks associated with generative AI. The introduction of the IBM Framework for Securing Generative AI marks a significant stride in safeguarding gen AI workflows throughout their lifecycle – from data collection to production deployment. This comprehensive framework offers guidance on potential security threats and recommends top defensive approaches, solidifying IBM’s commitment to advancing security in the era of generative AI.

Why Gen AI Security Matters:

IBM, a technology giant with a rich history in the security space, recognizes the multifaceted nature of risks that gen AI workloads present. While some risks align with those faced by other types of workloads, others are entirely novel. The three core tenets of IBM’s approach focus on securing the data, the model, and the usage, all underpinned by the essential elements of secure infrastructure and AI governance.

Securing Core Aspects:

Sridhar Muppidi, IBM Fellow and CTO at IBM Security, highlights the ongoing importance of core data security practices, such as access control and infrastructure security, in the realm of gen AI. However, he emphasizes that certain risks are unique to generative AI, such as data poisoning, bias, data diversity, data drift, and data privacy. An emerging area of concern is prompt injection, where malicious users attempt to modify a model’s output through manipulated prompts, requiring new controls for mitigation.

Navigating the Gen AI Security Landscape:

The IBM Framework for Securing Generative AI is not a standalone tool but a comprehensive set of guidelines and suggestions for securing gen AI workflows. The evolving nature of generative AI risks has given rise to new security categories, including Machine Learning Detection and Response (MLDR), AI Security Posture Management (AISPM), and Machine Learning Security Operation (MLSecOps).

MLDR involves scanning models to identify potential risks, while AISPM shares similarities with Cloud Security Posture Management, focusing on secure deployment through proper configurations and best practices. According to Muppidi, MLSecOps encompasses the entire lifecycle – from design to usage – ensuring the infusion of security into every stage.

IBM Introduces Innovative Analog AI Chip That Works Like a Human Brain

IBM has taken the wraps off a groundbreaking analog AI chip prototype, designed to mimic the cognitive abilities of the human brain and excel at intricate computations across diverse deep neural network (DNN) tasks.

This novel chip’s potential extends beyond its capabilities. IBM asserts that this cutting-edge creation has the potential to revolutionize artificial intelligence, significantly enhancing its efficiency and diminishing the power drain it imposes on computers and smartphones.

Unveiling this technological marvel in a publication from IBM Research, the company states, “The fully integrated chip features 64 AIMC cores interconnected via an on-chip communication network. It also implements the digital activation functions and additional processing involved in individual convolutional layers and long short-term memory units.”

A Paradigm Shift in AI Computing

Fashioned within the confines of IBM Albany NanoTech Complex, this new analog AI chip comprises 64 analog in-memory compute cores. Drawing inspiration from the operational principles of neural networks within biological brains, IBM has ingeniously incorporated compact, time-based analog-to-digital converters into every tile or core. This design enables seamless transitions between the analog and digital domains.

Furthermore, each tile, or core, is equipped with lightweight digital processing units adept at executing uncomplicated nonlinear neuronal activation functions and scaling operations, as elaborated upon in an August 10 blog post by IBM.

A Potential Substitution for Existing Digital Chips

In the not-so-distant future, IBM’s prototype chip may very well take the place of the prevailing chips propelling resource-intensive AI applications in computers and mobile devices. Elucidating this perspective, the blog post continues, “A global digital processing unit is integrated into the middle of the chip that implements more complex operations that are critical for the execution of certain types of neural networks.”

As the market witnesses a surge in foundational models and generative AI tools, the efficacy and energy efficiency of conventional computing methods upon which these models rely are confronting their limits.

IBM has set its sights on bridging this gap. The company contends that many contemporary chips exhibit a segregation between their memory and processing components, consequently stymying computational speed. This dichotomy forces AI models to be stored within discrete memory locations, necessitating constant data shuffling between memory and processing units.

Drawing a parallel with traditional computers, Thanos Vasilopoulos, a researcher based at IBM’s Swiss research laboratory, underscores the potency of the human brain. He emphasizes that the human brain achieves remarkable performance while consuming minimal energy.

According to Vasilopoulos, the heightened energy efficiency of the IBM chip could usher in an era where “hefty and intricate workloads could be executed within energy-scarce or battery-constrained environments,” such as automobiles, mobile phones, and cameras.

He further envisions that cloud providers could leverage these chips to curtail energy expenditures and reduce their ecological footprint.

IBM and NASA Release Geospatial Foundation Model on Hugging Face to Advance Climate Science and AI Applications

IBM and NASA have jointly announced the release of the Watsonx.ai geospatial foundation model on Hugging Face, a significant development that aims to harness the potential of vast amounts of satellite imagery to advance climate science and enhance life on Earth. The model was initially disclosed in February and is based on NASA’s Harmonized Landsat Sentinel-2 satellite data (HLS). It has undergone additional fine-tuning using labeled data for specific use cases like burn scar and flood mapping.

One of the key advantages of the geospatial foundation model lies in its utilization of enterprise technologies from IBM’s watsonx.ai initiative. Both organizations anticipate that the innovations introduced through this model will prove beneficial for scientific and business applications.

The foundation model’s most notable feature is its ability to address the challenge of data labeling at scale. Traditionally, AI training required extensive sets of labeled data. However, with foundation models, the AI is pre-trained on a large dataset of unlabeled data, and then fine-tuned using a smaller amount of labeled data for a specific use case. This approach allows for highly customized models and has demonstrated faster training and improved accuracy compared to models solely built with labeled data.

For example, when applied to flood prediction, the new foundation model achieved a 15% improvement in prediction accuracy using only half the amount of labeled data compared to a state-of-the-art model. Similarly, for the burn scar use case, the IBM model required 75% less labeled data than the current state-of-the-art model, resulting in significant performance enhancements.

IBM and NASA chose to make the geospatial foundation model available on Hugging Face due to the platform’s reputation as a leading community for open AI models. By doing so, they aim to foster its adoption and hope to gather insights and feedback from the community to further enhance the model’s capabilities over time.

In addition to benefiting scientists who work with satellite data, the model is expected to have implications for enterprise use cases of AI. IBM’s environment intelligence suite, which aids organizations with sustainability efforts, will eventually integrate the new model. Moreover, the experience gained from scientists fine-tuning the foundation model could lead to improvements in other areas of IBM’s AI development efforts through ‘meta learning.’

Overall, the release of the geospatial foundation model on Hugging Face represents a significant step in advancing AI applications for geospatial data analysis and holds promise for scientific and business communities alike.

IBM Enhances Adobe Collaboration for AI-Driven Content Supply Chains

IBM and Adobe are collaborating to enhance content supply chains through AI technology. IBM will expand its existing partnership with Adobe, utilizing Adobe Sensei GenAI services and Adobe Firefly, a suite of generative AI models, to assist clients in creating personalized customer experiences and customized journeys. IBM Consulting will introduce new Adobe consulting services to support clients in navigating the complex generative AI landscape.

The joint effort aims to establish an integrated content supply chain ecosystem, improving efficiency, task automation, and visibility for stakeholders involved in design and creative projects. By leveraging Adobe’s AI-accelerated Content Supply Chain solution and IBM’s consulting expertise, brands will be able to launch campaigns, experiences, and products with greater speed, confidence, and precision.

As part of the expanded partnership, Adobe’s enterprise customers will gain access to IBM Consulting’s team of experienced data and AI consultants, numbering 21,000 experts. These consultants will assist clients in implementing generative AI models into the design and creative process. The collaboration aims to maximize technology and workflows while ensuring transparency, explainability, and brand consistency by integrating Adobe’s AI-accelerated Content Supply Chain solution with clients’ proprietary customer data, brand guidelines, and intellectual property.

The services provided will involve the use of Firefly, initially focused on generating images and text effects, as well as Sensei GenAI services that serve as a copilot for marketers embedded in Adobe’s enterprise applications.

Enhancing Content Workflows with Generative AI

IBM said that its expanded partnership with Adobe aims to capitalize on the growing momentum in AI adoption, enabling brands to create highly personalized customer experiences that drive growth and productivity. With a focus on trust, transparency and brand consistency, the company said that the partnership seeks to redefine the possibilities of AI-powered experiences while elevating business decisions.

IBM’s Candy stated that IBM Consulting is collaborating closely with Adobe clients to assist them in preparing their internal data sources and structures. Additionally, they are helping clients identify suitable use cases, estimate the impact on value, and evaluate and recommend technologies to adopt.

“We are assisting our clients in training and customizing foundational models (FMs) and LLMs using both company and customer datasets,” he said. “We prioritize establishing guardrails to address bias and maintain a brand voice.”

Candy emphasized that IBM’s consultants have the expertise to use the complete generative AI technology stack, encompassing foundation models and over 50 domain-specific classical machine learning accelerators. This comprehensive range of tools enables them to expedite progress for clients.

“We use our unique IBM Garage method to co-create with clients and work together to build their ideas and bring them to enterprise scale,” he explained. “For example, we work with clients to develop prioritized AI use cases, define the technology roadmap, assets and tools to support those use cases, and develop the human-centric design and operating model needed to bring the use cases up to enterprise scale.”

IBM’s long-term vision for AI

IBM stated that its marketing transformation journey had laid the foundation for introducing these new services. The company has actively supported Adobe in enhancing its marketing team’s work management as part of its collaboration.

The expanded collaboration is built upon a strategic partnership of 20 years, which has encompassed technology and services. Notably, Adobe embraced Red Hat OpenShift, IBM AI and Sterling software as a result of this partnership.

The company highlighted that through its global client engagements, it has witnessed a shift in business approach from “plus AI” to “AI-first.” This transition signifies that AI is now deeply integrated into enterprises’ core operations.

IBM said it is actively reimagining work processes and fundamentally transforming tasks by leveraging AI technologies such as foundation models and generative AI.

“We’re helping clients around the globe and in every industry to embed AI in the ‘heartbeat’ processes of the enterprise,” said Candy. “Our experience tells us that getting to value for AI in business takes a deep understanding of the complexities involved in an enterprise and a human-centered, principled approach to using AI — and that won’t change anytime soon.”

IBM’s Quantum Leap: The Future Holds a 100,000-Qubit Supercomputer

IBM Aims for Unprecedented Quantum Computing Advancement: A 100,000-Qubit Supercomputer Collaboration with Leading Universities and Global Impact.

During the G7 summit in Hiroshima, Japan, IBM unveiled an ambitious $100 million initiative, joining forces with the University of Tokyo and the University of Chicago to construct a massive quantum computer boasting an astounding 100,000 qubits. This groundbreaking endeavor intends to revolutionize the computing field and unlock unparalleled possibilities across various domains.

Despite already holding the record for the largest quantum computing system with a 433-qubit processor, IBM’s forthcoming machine signifies a monumental leap forward in quantum capabilities. Rather than seeking to replace classical supercomputers, the project aims to synergize quantum power with classical computing to achieve groundbreaking advancements in drug discovery, fertilizer production, and battery performance.

IBM’s Vice President of Quantum, Jay Gambetta, envisions this collaborative effort as “quantum-centric supercomputing,” emphasizing the integration of the immense computational potential of quantum machines with the sophistication of classical supercomputers. By leveraging the strengths of both technologies, this fusion endeavors to tackle complex challenges that have long remained unsolvable. The initiative holds the potential to reshape scientific research and make significant contributions to the global scientific community.

Strides made for technological advancement

While significant progress has been made, the technology required for quantum-centric supercomputing is still in its infancy. IBM’s proof-of-principle experiments have shown promising results, demonstrating that integrated circuits based on CMOS technology can control cold qubits with minimal power consumption.

However, further innovations are necessary, and this is where collaboration with academic research institutions becomes crucial.

IBM’s modular chip design serves as the foundation for housing many qubits. With an individual chip unable to accommodate the sheer scale of qubits required, interconnects are being developed to facilitate the transfer of quantum information between modules.

IBM’s “Kookaburra,” a multichip processor with 1,386 qubits and a quantum communication link, is currently under development and anticipated for release in 2025. Additionally, the University of Tokyo and the University of Chicago actively contribute their expertise in components and communication innovations, making their mark on this monumental project.

As IBM embarks on this bold mission, it anticipates forging numerous industry-academic collaborations over the next decade. Recognizing the pivotal role of universities, Gambetta highlights the importance of empowering these institutions to leverage their strengths in research and development.

With the promise of a quantum-powered future on the horizon, the journey toward a 100,000-qubit supercomputer promises to unlock previously unimaginable scientific frontiers, revolutionizing our understanding of computation as we know it.

IBM Set to Revolutionize Data Security with Latest Quantum-Safe Technology

What Exactly Is Quantum-Safe Technology? And Why Is It Important? To understand this, we need to take a step back and look at what Quantum Computing is. Unlike Classical Computers, which store and process information using Binary Digits or Bits, Quantum Computers use Quantum Bits or Qubits, which can exist in multiple states simultaneously. This allows Quantum Computers to perform certain tasks, such as factoring large numbers, much faster than Classical Computers.

However, this also means that some of the Cryptographic Algorithms that are currently used to secure data, such as RSA and ECC, could be broken by Quantum Computers. This is where Quantum-Safe Technology comes in. It is a set of Cryptographic Algorithms that are resistant to attacks by Quantum Computers. It ensures that data remains secure in a post-quantum world.

Recently, IBM unveiled its “End-to-End Quantum-Safe Technology” at the annual Think Conference held in Orlando, Florida. IBM Quantum Safe is not just a single algorithm or tool. Rather, it is a comprehensive suite of tools and capabilities that can be used by organizations to secure their data. This includes Quantum-Safe Cryptography, which uses algorithms such as Lattice-Based Cryptography and Hash-Based Cryptography, as well as Post-Quantum Key Exchange Protocols.

What sets the IBM quantum-safe apart?

What sets IBM Quantum Safe apart is not just the technology itself. It is also IBM’s deep expertise in security. IBM has been working on quantum-safe cryptography for over a decade and has contributed to the development of many of the algorithms now considered quantum-safe. This means that IBM Quantum Safe is not just a theoretical concept but a practical solution tested and validated in real-world scenarios.

This is especially important for governmental agencies and businesses, which handle some of the most valuable and sensitive data. In a post-quantum world, the security of this data could be compromised if it is not protected by quantum-safe technology. IBM Quantum Safe provides these organizations with a way to future-proof their security and ensure that their data remains secure, even in the face of advances in quantum computing.

The announcement of IBM Quantum Safe has generated a lot of excitement in the technology industry. As quantum computing advances, the need for quantum-safe technology will only grow. IBM Quantum Safe provides a practical solution to this problem and has the potential to become the industry standard for post-quantum cryptography.

In her keynote address at the Think conference, Rometty emphasized the importance of quantum-safe technology in ensuring data security. “We are at an inflection point in our industry,” she said. “We need to ensure that our data remains secure in a post-quantum world. That is why we have developed IBM Quantum Safe – to provide a practical, comprehensive solution that can be used by organizations of all sizes and across all industries.”

With IBM’s deep expertise in security and its commitment to developing practical solutions, IBM Quantum Safe has the potential to become the gold standard for quantum-safe technology.