GTTSi’s Post

Artificial intelligence 🌐 is making its way into more and more areas of private and public life, including the energy sector. 🔸 Seeing as AI energy products need an excess of personal data to fully function, legislation is required to protect sensible information. Moreover, the risks of exposing a still vulnerable energy grid to unlegislated AI, especially in terms of potential data breaches, could prove devastating. #energy #energyindustry #zenonsoftware Read more: https://hubs.ly/Q02qVX540

Implementing AI security in organizations involves a multi-faceted approach that combines technical measures, governance, and human factors. Here are some key steps organizations can take: ### 1. Risk Assessment and Management   - **Identify Risks**: Conduct risk assessments for AI systems.   - **Mitigation Strategies**: Develop risk mitigation plans. ### 2. Secure Development Lifecycle   - **Secure Coding**: Follow secure coding practices.   - **Audits and Testing**: Regularly audit and test for vulnerabilities. ### 3. Data Security   - **Encryption**: Encrypt data at rest and in transit.   - **Integrity and Privacy**: Ensure data integrity and compliance with privacy regulations. ### 4. Model Security   - **Adversarial Robustness**: Implement defenses against adversarial attacks.   - **Monitoring**: Continuously monitor AI models. ### 5. Access Control and Authentication   - **RBAC**: Implement role-based access control.   - **MFA**: Use multi-factor authentication. ### 6. Explainability and Transparency   - **Model Explainability**: Use techniques like LIME or SHAP.   - **Decision Transparency**: Ensure AI decisions are understandable. ### 7. Incident Response   - **Response Plan**: Develop an incident response plan for AI.   - **Drills**: Conduct regular incident response drills. ### 8. Governance and Compliance   - **Governance Framework**: Establish AI governance.   - **Compliance**: Ensure adherence to regulations and ethical guidelines. ### 9. Employee Training and Awareness   - **Security Training**: Regular training on AI-specific threats.   - **Awareness Programs**: Educate employees on AI security importance. ### 10. Vendor and Supply Chain Security   - **Third-Party Management**: Assess risks from vendors.   - **Supply Chain Security**: Ensure security of third-party AI components. ### 11. Continuous Improvement   - **Feedback Loops**: Continuously improve AI security.   - **R&D**: Invest in research to counter emerging threats. ### 12. Tools and Technologies   - **SIEM**: Use Security Information and Event Management systems.   - **AI Security Tools**: Utilize specialized AI security tools. By integrating these practices, organizations can enhance the security of their AI systems, ensuring that they are robust against a variety of threats and that they operate in a secure and trustworthy manner. #cybersecurity #aisecurity #ai #artificialintelligence

𝗧𝗵𝗲 𝗨𝗻𝗶𝘁𝗲𝗱 𝗦𝘁𝗮𝘁𝗲𝘀, 𝗨𝗻𝗶𝘁𝗲𝗱 𝗞𝗶𝗻𝗴𝗱𝗼𝗺, 𝗮𝗻𝗱 𝘀𝗶𝘅𝘁𝗲𝗲𝗻 𝗼𝘁𝗵𝗲𝗿 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀 have taken a definitive step towards ensuring the 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗼𝗳 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜) by signing a detailed 20-page framework agreement. This initiative focuses on 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝘀𝗮𝗳𝗲𝘁𝘆 𝗮𝗻𝗱 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 into the AI development process to protect users and the public from potential risks. This multinational effort highlights the growing 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝘀𝗼𝗰𝗶𝗲𝘁𝘆 and the economy, prompting a proactive approach to 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. The agreement mandates 𝗿𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀, 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀, 𝗮𝗻𝗱 𝘀𝘁𝗿𝗶𝗰𝘁 𝘀𝗰𝗿𝘂𝘁𝗶𝗻𝘆 𝗼𝗳 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘃𝗲𝗻𝗱𝗼𝗿𝘀, although it remains an advisory document without legal authority. The key purpose of the agreement is to preemptively counter the 𝘁𝗵𝗿𝗲𝗮𝘁𝘀 𝗽𝗼𝘀𝗲𝗱 𝗯𝘆 𝗔𝗜, such as undermining democratic institutions, escalating fraudulent activities, or causing widespread economic disruption. However, the document stops short of addressing deeper issues such as the ethical implications of AI applications or the sourcing of data used by AI technologies. Initiated by the United States, this alliance seeks to balance rapid technological progress with an ethical and secure foundation for AI deployment, ultimately creating a dependable and safe AI environment Read more from Reuters:https://lnkd.in/gnbPgr6B 🌐🔒🤖 #machinelearning #datascience #data #iot #developer

The unique privacy challenges of AI According to data from Crunchbase, in 2023, over 25% of the investment in American start-ups has been directed towards companies specializing in AI. This wave of AI has brought forth unprecedented capabilities in data processing, analysis, and predictive modelling. However, AI introduces privacy challenges that are complex and multifaceted, different from those posed by traditional data processing: Data volume and variety. AI systems can digest and analyse exponentially more data than traditional systems, increasing the risk of personal data exposure. Predictive analytics. Through pattern recognition and predictive modelling, AI can infer personal behaviours and preferences, often without the individual’s knowledge or consent. Opaque decision-making. AI algorithms can make decisions affecting people’s lives without transparent reasoning, making tracing or challenging privacy invasions difficult. Data security. The large data sets AI requires to function effectively are attractive targets for cyber threats, amplifying the risk of breaches that could compromise personal privacy. Embedded bias. Without careful oversight, AI can perpetuate existing biases in the data it’s fed, leading to discriminatory outcomes and privacy violations. These challenges underscore the necessity for robust privacy protection measures in AI. Balancing the benefits of AI with the right to privacy requires vigilant design, implementation, and governance to prevent misuse of personal data.

This Forbes article titled "Will LLM Adoption Demand More Stringent Data Security Measures?" has sparked some interesting discussions. I'm grateful to Hessie Jones and #Forbes for giving me the opportunity to share my insights. Thank you for your time and consideration. The article discusses the increasing demand for data collection and the resulting challenges in data privacy and security, especially with the rise of technologies like LLMs. Key points: ⭕ Data breaches are on the rise ⭕ Rush to release new technologies ⭕ LLMs and indiscriminate data scraping ⭕ Regulation is emerging ⭕ Cyberattacks are more advanced ⭕ Startups need to be proactive ⭕ Data privacy tools are available ⭕ AI Ethics is now mainstream ⭕ Data security needs to keep up with AI advancements #llms #ai #genai #ml #nlp #aieithics #dataprivacy #datasecurity #compliance #privacyregulations #privacylaws #privacyengineering #cybersecurity #vulnerabilities #exploits #privacybydesign #technology #innovation

I met with Saima Fancy and Sahil Agarwal recently to tackle the new vulnerabilities enabled because of the rise of large language models. Some key areas in this article: • Countries with the most robust cybersecurity infrastructures include Finland, Norway, and Denmark. • US and Canada maintain strong defences in cybersecurity but only rank 9th and 10th overall when it comes to safety. • Countries with the highest risk: Venezuela, Honduras, Bolivia are much more vulnerable today to cyber espionage, and attacks on critical infrastructure and national safety, and the ensuing economic impacts. • Saima says organizations could legally opt to acquire data and train their models in a structured way, but instead "There's a race to release new technologies, which can inadvertently cause harm. It's often just a case of 'let's release it and see what happens." • Sahil says, "Since the introduction of ChatGPT, there has been a significant increase in awareness among the public, legislators, and companies about the potential risks of AI. This heightened awareness has surpassed much of what we've seen over the past decade, highlighting both the potential and the dangers of AI technologies.” • LLMs have enabled attackers to conduct more effective social engineering attacks by generating personalized messages or responses based on more extensive knowledge of the target’s online activity, their preferences and behaviors. • The speed at which LLMS can also automate certain stages of cyber-attacks by generating targeted queries, crafting exploit payloads, or bypassing security measures, is making it increasingly difficult to identify these before they’re implemented. Read more below: Note: Linkedin is not properly posting this URL so have added the image and title just for context https://lnkd.in/g4w76EBu Will LLM Adoption Demand More Stringent Data Security Measures?

In December, I had the privilege of co-organizing the Multi-Agent AI Security workshop at the #neurips2023 conference in New Orleans. Here are some of the key insights and experiences from this event: 🧠 The Rationale Behind the Workshop The rapid advancement of AI technologies, such as ChatGPT, promises significant economic and societal benefits. However, the impending deployment of these advanced AI systems also raises a host of concerns, ranging from robustness and fairness to more extreme scenarios like physical safety. The pace at which these systems are developing often outstrips the ability to incorporate essential security principles. Our workshop aimed to bridge the gap between the AI and Information Security communities, which currently lack sufficient interconnection to address both immediate and future threats. Our goal was to create a roadmap for the future of AI security through a series of expert discussions, industry practitioner interventions, keynote speeches, and contributed research content. 🗣 Panel Discussion I had the opportunity to lead a panel debate on AI security, safety, and ethics. The panel featured an engaging conversation with Sanja Šćepanović-Stojanović, PhD, Stephen McAleer, Adam Gleave, and Esben K.. The discussion revolved around several key questions: 👉 How can we effectively incorporate cybersecurity principles into the foundational design of AI systems to ensure robust and resilient applications? 👉 What are the emerging cybersecurity threats specifically targeting AI systems, and how can we mitigate them? 👉 What role should government regulation play in guiding the integration of AI in cybersecurity, and how can policies be shaped to foster innovation while ensuring security? 👉 What are the potential long-term security and safety implications of unregulated AI-to-AI interactions, and how can regulators anticipate and address these consequences? 👩💻 👨💻 Diversifying the Workforce Another significant aspect of my participation in the workshop was the opportunity to engage in discussions about diversifying the workforce in the information security and AI fields. These conversations provided invaluable insights into the challenges and opportunities in this domain. The importance of diversity in these fields cannot be overstated, as it fosters innovation, enhances problem-solving capabilities, and ensures a broader perspective in the development and application of AI technologies. For those interested, the live recordings will be made available shortly. Stay tuned! 😎 The list of accepted papers: https://lnkd.in/ePd2K9gw Hawra Milani Swapneel Mehta Christian Schroeder de Witt Martin Strohmeier Carla Zoe Cremer

The new AI executive order is packed with vital steps to shape our digital future. Let's break it down: 🌐 The order establishes new AI safety and security standards across the public and private sectors 💻 Developers of powerful AI systems must share safety test results with the U.S. government 🛡️ The National Institute of Standards and Technology is setting rigorous red-team testing standards 🌟 The order creates a new AI Safety and Security Board 🌐 Cybersecurity Boost: An advanced program will develop AI tools to fix software vulnerabilities 🕵️♀️ The U.S. military and intelligence community will also safely utilize AI Other Key Takeaways: 🌍 The U.S. is advancing AI research in healthcare, climate change, and more 📊 AI researchers and students get a new pilot program, the National AI Research Resource 🧠 🤝 International collaboration is key: State and Commerce Depts. are on it 👀 VP Kamala Harris is also attending the UK Summit on AI Safety - with China as a controversial guest 👾 Ethical AI deployment is a top priority, as well as addressing concerns about human rights Check out more of my initial takeaways below! Information Security Media Group (ISMG)

UK AI Safety Institute Warning on Harmful Outputs of GenAI Large Language Models Following on from the Cisco Data Privacy Report {Source: https://lnkd.in/gsMkWCbj } this latest report from the UK government (AI Safety Institute) highlights other risks to be managed when deploying GenAI solutions {Source: https://lnkd.in/gjCieArb } 💡 68% of those surveyed by Cisco thought information entered could be shared publicly or with competitors 💡 The AISI report focussed on GenAI performance across tasks and how secure it was from being asked to provide harmful outputs 💡 The built-in safeguards found within five large language models released by “major labs” are ineffective to prevent jailbreak attacks 💡 A jailbreak attack is a form of hacking which aims to bypass an AI model's ethical safeguards and elicit prohibited information 💡 Getting LLM to pretend to be another new LLM to perform a jailbreak is suggested by many on Google search ⏫ 93% of cybersecurity leaders say their companies have deployed GenAI in another survey by Splunk {Source: https://lnkd.in/gejWArzb } 🔽 34% have not erected safeguards against security breaches ⏫ Moody's Investors Service noted that cyberattacks annually have increased by 26% on average from 2017 to 2023. 🔚 The Case for AI is still overwhelming for streamlining process, increasing productivity, IP monetisation, email management, unstructured data processing, financial reconciliation, dispute process handling, legal and credit document reconciliation. 🔚 🔝 Ask us which AI solutions are best at these tasks above. If Privacy and Accuracy are key, then look outside Generative AI for your AI solution. 🔝 Ask us for more information here. For more insights on generational finance perspectives, please follow us {https://lnkd.in/gM35Nui2 #AI #AIdevelopment #AIinvestment #AIefficiency #AIaccuracy #SixSigma #FinancialMarkets #OperationalEfficiency #Businessstrategy #Commercialstrategy

The MENA region has shown a lot of excitement recently with the rapid development of artificial intelligence (AI) technologies, including recent funding rounds successfully closed by intella, Pony.ai, and MALY. While there is a lot of optimism around the technological progress that AI can create, it’s also imperative to understand the risks involved with using these new technologies and their implementation. PwC outlines some keys risks to consider when implementing AI systems as a founder or business leader and steps you can take to address and mitigate possible inherent risks associated with these technologies.