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A new era of cybersecurity with AI: Predictions for 2024

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A new era of cybersecurity with AI: Predictions for 2024

Artificial intelligence (AI) has been table stakes in cybersecurity for several years now, but the broad adoption of Large Language Models (LLMs) made 2023 an especially exciting year. In fact, LLMs have already started transforming the entire landscape of cybersecurity. However, it is also generating unprecedented challenges.

On one hand, LLMs make it easy to process large amounts of information and for everybody to leverage AI. They can provide tremendous efficiency, intelligence, and scalability for managing vulnerabilities, preventing attacks, handling alerts, and responding to incidents.

On the other hand, adversaries can also leverage LLMs to make attacks more efficient, exploit additional vulnerabilities introduced by LLMs, and misuse of LLMs can create more cybersecurity issues such as unintentional data leakage due to the ubiquitous use of AI.

Deployment of LLMs requires a new way of thinking about cybersecurity. It is a lot more dynamic, interactive, and customized. During the days of hardware products, hardware was only changed when it was replaced by the next new version of hardware. In the era of cloud, software could be updated and customer data were collected and analyzed to improve the next version of software, but only when a new version or patch was released.

Now, in the new era of AI, the model used by customers has its own intelligence, can keep learning, and change based on customer usage — to either better serve customers or skew in the wrong direction. Therefore, not only do we need to build security in design – make sure we build secure models and prevent training data from being poisoned — but also continue evaluating and monitoring LLM systems after deployment for their safety, security, and ethics.

Most importantly, we need to have built-in intelligence in our security systems (like instilling the right moral standards in children instead of just regulating their behaviors) so that they can be adaptive to make the right and robust judgment calls without drifting away easily by bad inputs.

What have LLMs brought for cybersecurity, good or bad? I will share what we have learned in the past year and my predictions for 2024.

Looking back in 2023

When I wrote The Future of Machine Learning in Cybersecurity a year ago (before the LLM era), I pointed out three unique challenges for AI in cybersecurity: accuracy, data shortage, and lack of ground truth, as well as three common AI challenges but more severe in cybersecurity: explainability, talent scarcity, and AI security.

Now, a year later after lots of explorations, we identify LLMs’ big help in four out of these six areas: data shortage, lack of ground truth, explainability, and talent scarcity. The other two areas, accuracy, and AI security, are extremely critical yet still very challenging.

I summarize the biggest advantages of using LLMs in cybersecurity in two areas:

1. Data

Labeled data

Using LLMs has helped us overcome the challenge of not having enough “labeled data”.

High-quality labeled data are necessary to make AI models and predictions more accurate and appropriate for cybersecurity use cases. Yet, these data are hard to come by. For example, it is hard to uncover malware samples that allow us to learn about attack data. Organizations that have been breached aren’t exactly excited about sharing that information.

LLMs are helpful at gathering initial data and synthesizing data based on existing real data, expanding upon it to generate new data about attack sources, vectors, methods, and intentions, This information is then used to build for new detections without limiting us to field data.

Ground truth

As mentioned in my article a year ago, we don’t always have the ground truth in cybersecurity. We can use LLMs to improve ground truth dramatically by finding gaps in our detection and multiple malware databases, reducing False Negative rates, and retraining models frequently.

2. Tools

LLMs are great at making cybersecurity operations easier, more user-friendly, and more actionable. The biggest impact of LLMs on cybersecurity so far is for the Security Operations Center (SOC).

For example, the key capability behind SOC automation with LLM is function calling, which helps translate natural language instructions to API calls that can directly operate SOC. LLMs can also assist security analysts in handling alerts and incident responses much more intelligently and faster. LLMs allow us to integrate sophisticated cybersecurity tools by taking natural language commands directly from the user.

Explainability

Previous Machine Learning models performed well, but could not answer the question of “why?” LLMs have the potential to change the game by explaining the reason with accuracy and confidence, which will fundamentally change threat detection and risk assessment.

LLMs’ capability to quickly analyze large amounts of information is helpful in correlating data from different tools: events, logs, malware family names, information from Common Vulnerabilities and Exposures (CVE), and internal and external databases. This will not only help find the root cause of an alert or an incident but also immensely reduce the Mean Time to Resolve (MTTR) for incident management.

Talent scarcity

The cybersecurity industry has a negative unemployment rate. We don’t have enough experts, and humans cannot keep up with the massive number of alerts. LLMs reduce the workload of security analysts enormously thanks to LLMs’ advantages: assembling and digesting large amounts of information quickly, understanding commands in natural language, breaking them down into necessary steps, and finding the right tools to execute tasks.

From acquiring domain knowledge and data to dissecting new samples and malware, LLMs can help expedite building new detection tools faster and more effectively that allow us to do things automatically from identifying and analyzing new malware to pinpointing bad actors.

We also need to build the right tools for the AI infrastructure so that not everybody has to be a cybersecurity expert or an AI expert to benefit from leveraging AI in cybersecurity.

3 predictions for 2024

When it comes to the growing use of AI in cybersecurity, it’s very clear that we are at the beginning of a new era – the early stage of what’s often called “hockey stick” growth. The more we learn about LLMs that allow us to improve our security posture, the better the likelihood we will be ahead of the curve (and our adversaries) in getting the most out of AI.

While I think there are a lot of areas in cybersecurity ripe for discussion about the growing use of AI as a force multiplier to fight complexity and widening attack vectors, three things stand out:

1. Models

AI models will make huge steps forward in the creation of in-depth domain knowledge that is rooted in cybersecurity’s needs.

Last year, there was a lot of attention devoted to improving general LLM models. Researchers worked hard to make models more intelligent, faster, and cheaper. However, there exists a huge gap between what these general-purpose models can deliver and what cybersecurity needs.

Specifically, our industry doesn’t necessarily need a huge model that can answer questions as diverse as “How to make Eggs Florentine” or “Who discovered America”. Instead, cybersecurity needs hyper-accurate models with in-depth domain knowledge of cybersecurity threats, processes, and more.

In cybersecurity, accuracy is mission-critical. For example, we process 75TB+ amount of data every day at Palo Alto Networks from SOCs around the world. Even 0.01% of wrong detection verdicts can be catastrophic. We need high-accuracy AI with a rich security background and knowledge to deliver tailored services focused on customers’ security requirements. In other words, these models need to conduct fewer specific tasks but with much higher precision.

Engineers are making great progress in creating models with more vertical-industry and domain-specific knowledge, and I’m confident that a cybersecurity-centric LLM will emerge in 2024.

2. Use cases

Transformative use cases for LLMs in cybersecurity will emerge. This will make LLMs indispensable for cybersecurity.

In 2023, everybody was super excited about the amazing capabilities of LLMs. People were using that “hammer” to try every single “nail”.

In 2024, we will understand that not every use case is the best fit for LLMs. We will have real LLM-enabled cybersecurity products targeted at specific tasks that match well with LLMs’ strengths. This will truly increase efficiency, improve productivity, enhance usability, solve real-world issues, and reduce costs for customers.

Imagine being able to read thousands of playbooks for security issues such as configuring endpoint security appliances, troubleshooting performance problems, onboarding new users with proper security credentials and privileges, and breaking down security architectural design on a vendor-by-vendor basis.

LLMs’ ability to consume, summarize, analyze, and produce the right information in a scalable and fast way will transform Security Operations Centers and revolutionize how, where, and when to deploy security professionals.

3. AI security and safety

In addition to using AI for cybersecurity, how to build secure AI and secure AI usage, without jeopardizing AI models’ intelligence, are big topics. There have already been many discussions and great work in this direction. In 2024, real solutions will be deployed, and even though they might be preliminary, they will be steps in the right direction. Also, an intelligent evaluation framework needs to be established to dynamically assess the security and safety of an AI system.

Remember, LLMs are also accessible to bad actors. For example, hackers can easily generate significantly larger numbers of phishing emails at much higher quality using LLMs. They can also leverage LLMs to create brand-new malware. But the industry is acting more collaboratively and strategically in the usage of LLMs, helping us get ahead and stay ahead of the bad guys.

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Artificial Intelligence

Two AIs Get Chatty: A Big Leap at UNIGE

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Chatting AIs: How It All Started

Scientists at the University of Geneva (UNIGE) have done something super cool. They’ve made an AI that can learn stuff just by hearing it and then can pass on what it’s learned to another AI. It’s like teaching your friend how to do something just by talking to them. This is a big deal because it’s kind of like how we, humans, learn and share stuff with each other, but now machines are doing it too!

Two AIs Get Chatty By Taking Cues from Our Brains

This whole idea came from looking at how our brains work. Our brains have these things called neurons that talk to each other with electrical signals, and that’s how we learn and remember things. The UNIGE team made something similar for computers, called artificial neural networks. These networks help computers understand and use human language, which is pretty awesome.

How Do AIs Talk to Each Other?

For a long time, getting computers to learn new things just from words and then teach those things to other computers was super hard. It’s easy for us humans to learn something new, figure it out, and then explain it to someone else. But for computers? Not so much. That’s why what the UNIGE team did is such a big step forward. They’ve made it possible for one AI to learn a task and then explain it to another AI, all through chatting.

two ais get chatty cool

Learning Like Us

The secret here is called Natural Language Processing (NLP). NLP is all about helping computers understand human talk or text. This is what lets AIs get what we’re saying and then do something with that info. The UNIGE team used NLP to teach their AI how to understand instructions and then act on them. But the real magic is that after learning something new, this AI can turn around and teach it to another AI, just like how you might teach your friend a new trick.

Breaking New Ground in AI Learning

The UNIGE team didn’t just stop at making an AI that learns from chatting. They took it a step further. After one AI learns a task, it can explain how to do that task to another AI. Imagine you figured out how to solve a puzzle and then told your friend how to do it too. That’s what these AIs are doing, but until now, this was super hard to achieve with machines.

From Learning to Teaching

The team started with a really smart AI that already knew a lot about language. They hooked it up to a simpler AI, kind of like giving it a buddy to chat with. First, they taught the AI to understand language, like training it to know what we mean when we talk. Then, they moved on to getting the AI to do stuff based on what it learned from words alone. But here’s the kicker: after learning something new, this AI could explain it to its buddy AI in a way that the second one could get it and do the task too.

A Simple Task, A Complex Achievement

The tasks themselves might seem simple, like identifying which side a light was flashing on. But it’s not just about the task; it’s about understanding and teaching it, which is huge for AI. This was the first time two AIs communicated purely through language to share knowledge. It’s like if one robot could teach another robot how to dance just by talking about it. Pretty amazing, right?

Why This Matters

This is a big deal for the future. It’s not just about AIs chatting for fun; it’s about what this means for robots and technology down the line. Imagine robots that can learn tasks just by listening to us and then teach other robots how to do those tasks. It could change how we use robots in homes, hospitals, or even in space. Instead of programming every single step, we could just tell them what we need, and they’d figure it out and help each other out too. It’s like having a team of robots that learn from each other and us, making them way more useful and flexible.

The UNIGE team is already thinking about what’s next. Their AI network is still pretty small, but they believe they can make it bigger and better. We’re talking about robots that not only understand and learn from us but also from each other. This could lead to robots that are more like partners, helping solve problems, invent new things, and maybe even explore the universe with us.

What’s the Future?

This adventure isn’t just about what’s happening now. It’s about opening the door to a future where robots really get us, and each other. The UNIGE team’s work is super exciting for anyone who’s into robots. It’s all about making it possible for machines to have chats with each other, which is a big deal for making smarter, more helpful robots.

The brains behind this project say they’ve just started. They’ve got a small network of AI brains talking, but they’re dreaming big. They’re thinking about making even bigger and smarter networks. Imagine humanoid robots that don’t just understand what you’re telling them but can also share secrets with each other in their own robot language. The researchers are pretty stoked because there’s nothing stopping them from turning this dream into reality.

So, we’re looking at a future where robots could be our buddies, understanding not just what we say but also how we say it. They could help out more around the house, be there for us when we need someone to talk to, or even work alongside us, learning new things and teaching each other without us having to spell it all out. It’s like having a robot friend who’s always there to learn, help, and maybe even make us laugh.

Wrap up

What started as a project at UNIGE could end up changing how we all live, work, and explore. It’s a glimpse into a future where AIs and robots are more than just tools; they’re part of our team, learning and growing with us. Who knows what amazing things they’ll teach us in return?

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Artificial Intelligence

KASBA.AI Now Available on ChatGPT Store

ChatGPT Store by OpenAI is the new platform for developers to create and share unique AI models with monetization opportunities

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OpenAI, the leading Artificial Intelligence research laboratory has taken a significant step forward with the launch of the ChatGPT Store. This new platform allows developers to create and share their unique AI models, expanding the capabilities of the already impressive ChatGPT. Among the exciting additions to the store are Canva, Veed, Alltrails and now KASBA.AI with many more entering every day.

About OpenAI

OpenAI, founded by Elon Musk and Sam Altman, has always been at the forefront of AI research. With a mission to ensure that artificial general intelligence benefits all of humanity, they have consistently pushed the boundaries of what is possible in the field.

OpenAI’s ChatGPT has already changed the way we interact with technology with its ability to generate coherent and contextually relevant responses. Now, with the ChatGPT Store, OpenAI is aiming to empower developers and non technical users to contribute and build upon this powerful platform.

kasba.ai chatgpt store

What is the ChatGPT Store?

The ChatGPT Store is an exciting initiative that allows developers to create, share and in time monetise their unique AI models. It serves as a marketplace for AI models that can be integrated with ChatGPT.

This means that users can now have access to a wide range of specialised conversational AI models, catering to their specific needs. The ChatGPT Store opens up a world of possibilities, making AI more accessible and customisable than ever before.

chatgpt store

Key Features of the ChatGPT Store

Some unique features of the store include customisable AI models, pre trained models for quick integration and the ability for developers to earn money by selling their models.

Developers can also leverage the rich ecosystem of tools and resources provided by OpenAI to enhance their models. This collaborative marketplace fosters innovation and encourages the development of conversational AI that can cater to various industries and use cases.

Impact on Industries and Society

The launch of the ChatGPT Store has far reaching implications for industries and society as a whole. By making AI models more accessible and customisable, businesses can now leverage conversational AI to enhance customer support, automate repetitive tasks and improve overall efficiency.

From healthcare and finance to education and entertainment the impact of AI on various sectors will only grow with the availability of specialised models on the ChatGPT Store. This democratisation of conversational AI technology will undoubtedly pave the way for a more connected and efficient world.

Ethical Considerations

As with any technological advancement, ethical considerations are crucial. OpenAI places a strong emphasis on responsible AI development and encourages developers to adhere to guidelines and principles that prioritize user safety and privacy. The ChatGPT Store ensures that AI models are vetted and reviewed to maintain high standards.

OpenAI is committed to continuously improving the user experience, and user feedback plays a vital role in shaping the future of AI development. For specific concerns regarding AI and data protection visit Data Protection Officer on ChatGPT Store.

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KASBA.AI on ChatGPT Store

One of the most exciting additions to the ChatGPT Store is KASBA.AI, your guide to the latest AI tool reviews, news, AI governance and learning resources. From answering questions to providing recommendations, KASBA.AI hopes to deliver accurate and contextually relevant responses. Its advanced algorithms and state of the art natural language processing make it a valuable asset to anyone looking for AI productivity tools in the marketplace.

Takeaway

OpenAI’s ChatGPT Store represents an exciting leap forward in the world of conversational AI. With customisable models, the ChatGPT Store empowers developers to create AI that caters to specific needs, with the potential to propel industries and society to new horizons..

OpenAI’s commitment to responsible AI development should ensure that user safety and privacy are prioritised; lets keep an eye here! Meanwhile as we traverse this new era of conversational AI, ChatGPT Store will undoubtedly shape the future of how we interact with technology in time to come with potentially infinite possibilities.

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Lets Govern AI Rather Than Let It Govern Us

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A pivotal moment has unfolded at the United Nations General Assembly. For the first time, a resolution was adopted focused on ensuring Artificial Intelligence (AI) systems are “safe, secure and trustworthy”, marking a significant step towards integrating AI with sustainable development globally. This initiative, led by the United States and supported by an impressive cohort of over 120 other Member States, underscores a collective commitment to navigating the AI landscape with the utmost respect for human rights.

But why does this matter to us, the everyday folks? AI isn’t just about robots from sci-fi movies anymore; it’s here, deeply embedded in our daily lives. From the recommendations on what to watch next on Netflix to the virtual assistant in your smartphone, AI’s influence is undeniable. Yet, as much as it simplifies tasks, the rapid evolution of AI also brings forth a myriad of concerns – privacy issues, ethical dilemmas and the very fabric of our job market being reshaped.

The Unanimous Call for Responsible AI Governance

The resolution highlights a crucial understanding: the rights we hold dear offline must also be protected in the digital realm, throughout the lifecycle of AI systems. It’s a call to action for not just countries but companies, civil societies, researchers, and media outlets to develop and support governance frameworks that ensure the safe and trustworthy use of AI. It acknowledges the varying levels of technological development across the globe and stresses the importance of supporting developing countries to close the digital divide and bolster digital literacy.

The United States Ambassador to the UN, Linda Thomas-Greenfield, shed light on the inclusive dialogue that led to this resolution. It’s seen as a blueprint for future discussions on the challenges AI poses, be it in maintaining peace, security, or responsible military use. This resolution isn’t about stifling innovation; rather, it’s about ensuring that as we advance, we do so with humanity, dignity, and a steadfast commitment to human rights at the forefront.

This unprecedented move by the UN General Assembly is not just a diplomatic achievement; it’s a global acknowledgment that while AI has the potential to transform our world for the better, its governance cannot be taken lightly. The resolution, co-sponsored by countries including China, represents a united front in the face of AI’s rapid advancement and its profound implications.

Bridging the Global Digital Divide

As we stand at this crossroads, the message is clear: the journey of AI is one we must steer with care, ensuring it aligns with our shared values and aspirations. The resolution champions a future where AI serves as a force for good, propelling us towards the Sustainable Development Goals, from eradicating poverty to ensuring quality education for all.

aiunitednations

The emphasis on cooperation, especially in aiding developing nations to harness AI, underscores a vision of a world where technological advancement doesn’t widen the gap between nations but brings us closer to achieving global equity. It’s a reminder that in the age of AI, our collective wisdom, empathy, and collaboration are our most valuable assets.

Ambassador Thomas-Greenfield’s remarks resonate with a fundamental truth: the fabric of our future is being woven with threads of artificial intelligence. It’s imperative that we, the global community, hold the loom. The adoption of this resolution is not the end, but a beginning—a stepping stone towards a comprehensive framework where AI enriches lives without compromising our moral compass.

At the heart of this resolution is the conviction that AI, though devoid of consciousness, must operate within the boundaries of our collective human conscience. The call for AI systems that respect human rights isn’t just regulatory rhetoric; it’s an appeal for empathy in algorithms, a plea to encode our digital evolution with the essence of what it means to be human.

This brings to light a pertinent question: How do we ensure that AI’s trajectory remains aligned with human welfare? The resolution’s advocacy for cooperation among nations, especially in supporting developing countries, is pivotal. It acknowledges that the AI divide is not just a matter of technological access but also of ensuring that all nations have a voice in shaping AI’s ethical landscape. By fostering an environment where technology serves humanity universally, we inch closer to a world where AI’s potential is not a privilege but a shared global heritage.

Furthermore, the resolution’s emphasis on bridging the digital divide is a clarion call for inclusivity in the digital age. It’s a recognition that the future we craft with AI should be accessible to all, echoing through classrooms in remote villages and boardrooms in bustling cities alike. The initiative to equip developing nations with AI tools and knowledge is not just an act of technological philanthropy; it’s an investment in a collective future where progress is measured not by the advancements we achieve but by the lives we uplift.

Uniting for a Future Shaped by Human Values

The global consensus on this resolution, with nations like the United States and China leading the charge, signals a watershed moment in international diplomacy. It showcases a rare unity in the quest to harness AI’s potential responsibly, amidst a world often divided by digital disparities. The resolution’s journey, from conception to unanimous adoption, reflects a world waking up to the reality that in the age of AI, our greatest strength lies in our unity.

As we stand at the dawn of this new era, the resolution serves as both a compass and a beacon; a guide to navigate the uncharted waters of AI governance and a light illuminating the path to a future where technology and humanity converge in harmony. The unanimous adoption of this resolution is not just a victory for diplomacy; it’s a promise of hope, a pledge that in the symphony of our future, technology will amplify, not overshadow, the human spirit.

In conclusion, “Let’s Govern AI Rather Than Let It Govern Us” is more than a motto; it’s a mandate for the modern world. It’s a call to action for every one of us to participate in shaping a future where AI tools are wielded with wisdom, wielded to weave a tapestry of progress that reflects our highest aspirations and deepest values.

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