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Forrester, May 24, 2023
The evolving landscape of artificial intelligence (AI) presents significant challenges to security and safety. In a recent report titled “Top Cybersecurity Threats in 2023,” the pressing need for security leaders to defend AI models against real threats already present in the field was highlighted. The emergence of SaaS LLMs (Software-as-a-Service Language Models) introduces third-party risks that demand effective risk management from security teams. While third-party breaches are relatively rare, it is crucial not to underestimate the potential consequences.
Moreover, the adoption of generalized models from major players such as Microsoft, Anthropic, and Google raises another major concern. While it may seem like a quick solution to implement these models, security leaders and their teams face a more significant challenge. The vulnerability lies in fine-tuned models, which utilize sensitive and confidential corporate data. The responsibility for protecting this data rests with security teams, and the time to act is not in the distant future but rather an urgent matter. According to Forrester, fine-tuned models will proliferate across enterprises, devices, and individuals, necessitating comprehensive protection measures.
Addressing the security and safety of AI requires a thorough understanding of potential threats. Attacks such as model theft, inference attacks, data poisoning, and prompt injection can compromise the integrity and functionality of AI and ML models. By stealing models, adversaries can nullify the competitive advantage of organizations. Inference attacks pose risks of data leakage, while data poisoning can result in inaccurate or unwanted outcomes. Prompt injection, a new concern, allows attackers to exploit generative AI and manipulate applications in unforeseen ways. These threats need to be addressed comprehensively to ensure the security and safety of AI systems.
In conclusion, the security and safety of AI models require vigilant protection against emerging threats. Security leaders must be proactive in defending against risks posed by SaaS LLMs and the use of generalized models. Additionally, safeguarding fine-tuned models, which contain sensitive corporate data, is of utmost importance. Understanding and countering potential attacks, such as model theft, inference attacks, data poisoning, and prompt injection, is crucial for ensuring the integrity and functionality of AI systems. By taking the necessary precautions and implementing robust security measures, organizations can mitigate risks and foster a secure AI environment.
As generative artificial intelligence (GenAI) continues to revolutionize various sectors, ensuring the security and safety of AI systems has become paramount. The widespread adoption of this groundbreaking technology requires constant vigilance and swift adaptations from AI developers, business users, investors, policymakers, and citizens alike. It is crucial to manage the inherent risks associated with GenAI comprehensively, taking into account factors such as privacy, cybersecurity, regulatory compliance, third-party relationships, legal obligations, and intellectual property.
To fully harness the potential benefits of GenAI, organizations must focus on balancing risks with innovation to foster trust within their company and gain a competitive advantage. This necessitates the involvement of risk professionals who can guide the safe and secure implementation of generative AI. These experts ensure that GenAI systems uphold privacy standards, mitigate harmful biases, validate reliability, and remain accountable, transparent, and interpretable. By building trust into the foundation of AI initiatives, companies can establish a “trust-by-design” approach that resonates with customers, investors, business partners, employees, and society as a whole.
While everyone has a role to play, key C-suite leaders hold particular responsibility in activating responsible AI practices. Their leadership is instrumental in embedding trust within the organization’s AI framework and prioritizing customer-centric solutions while considering societal interests. By addressing the amplified risks associated with GenAI, such as sophisticated phishing attempts, data and privacy concerns, compliance challenges, legal risks, and financial vulnerabilities, these leaders ensure the safe and responsible deployment of generative AI technologies.
Overall, the security and safety of AI systems, particularly in the context of generative AI, is paramount. Organizations must leverage the expertise of risk professionals, collaborate across departments, and prioritize governance and oversight to instill trust in their AI initiatives. By striking a balance between risk management and innovation, companies can navigate the complexities of GenAI and unlock its transformative potential while safeguarding privacy, security, and ethical considerations.
Daniel Miessler Blog, May 15, 2023
As artificial intelligence (AI) rapidly advances and integrates into various aspects of our lives, it is imperative to prioritize the security and safety of AI systems. The evolving landscape of AI presents new challenges and vulnerabilities that need to be addressed. While previous discussions have primarily focused on attacking pre-ChatGPT AI systems and machine learning implementations, the emergence of integration technologies like Langchain has necessitated a comprehensive understanding of the security landscape surrounding AI. To ensure the responsible and ethical deployment of AI, it is crucial to develop strategies that protect against potential threats and vulnerabilities.
A critical aspect of AI security revolves around the effective assessment of AI-based systems. This requires going beyond considering the models alone and understanding the entire AI-powered ecosystem. AI attack surfaces extend beyond models and encompass components such as AI Assistants, Agents, Tools, Models, and Storage. AI Assistants, which are increasingly becoming integral to our daily lives, require vast amounts of personal data to function optimally. Consequently, attacking these assistants can yield significant leverage for malicious actors. Additionally, Agents and Tools within AI systems can be targeted, allowing attackers to exploit vulnerabilities and gain unauthorized access to critical resources.
One of the key challenges in securing AI lies in understanding and mitigating the risks associated with different components. Attacking models has been an area of focus in the AI security space, with researchers exploring methods to manipulate model behavior, introduce bias, or compromise the trustworthiness of results. Storage mechanisms, such as vector databases, also present potential vulnerabilities that can be exploited. It is crucial to identify and address these risks proactively to ensure the security, privacy, and ethical use of AI technology. By comprehensively assessing and defending against potential attacks across the AI ecosystem, we can foster a safe and trustworthy AI-powered future.
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