The advent of Generative AI (GenAI) has been nothing short of transformative —from revolutionizing industries to elevating capabilities in content creation, natural language processing (NLP), and automated decision-making, and more. Industry leaders such as Amazon CEO Andy Jassey have acknowledged its massive potential— stating that GenAI has already saved the company millions of dollars. This is only the beginning.
As organizations increasingly adopt Gen(AI), they are presented with unprecedented opportunities. However, with these advancements comes a parallel challenge: safeguarding this technology and the data it depends on. As Gen(AI) transforms the business landscape, it also introduces new security concerns. Even organizations with robust cybersecurity foundations are struggling to address the evolving nature of AI-specific threats. As Gen(AI) security becomes an enterprise priority, this blog explores key security practices and initiatives.
Addressing AI Security Challenges with CoSAI
Recognizing the magnitude of these challenges, a new initiative has emerged: the Coalition for Secure AI (CoSAI). This coalition, housed under OASIS Open, a global standards and open-source consortium, aims to create comprehensive security measures to mitigate the unique risks associated with AI. Founding members of CoSAI include Amazon, Anthropic, Chainguard, Cisco, Cohere, IBM, Intel, Microsoft, NVIDIA, OpenAI, and others. CoSAI brings together a diverse range of stakeholders, including industry leaders, academics, and other experts, to address the fragmented landscape of AI security.
The timing of CoSAI’s formation could not be more crucial. With global cost of cybercrime predicted to reach $10.5 trillion annually by 2025, securing GenAI adoption is imperative. As such, the deployment of GenAI technology is not just a business imperative but a global societal one. The consequences of failing to secure AI could be devastating for organizations, governments, and economies.
The Current Security Landscape for Large Language Models
For organizations, especially those in highly regulated industries, incorporating GenAI into critical business operations poses the threat of exposing sensitive information, mandating a cautious approach. So, how do organizations ensure that they can leverage GenAI’s full potential while minimizing security risks? A comprehensive AI security approach is a necessary first step — one that is tailored to emerging threats and new vulnerabilities introduced by large language models (LLMs).
The Open Worldwide Application Security Project (OWASP) has identified the top 10 critical security issues associated with LLMs, providing insight into the most pressing risks:
- Prompt Injection Attacks: Malicious actors can craft prompts to manipulate model outputs or trigger unintended behavior.
- Insecure Output Handling: Unintended consequences such as remote code execution can arise when the output of LLMs isn’t properly sanitized or validated.
- Training Data Poisoning: Attackers may intentionally contaminate training datasets to influence the behavior of AI models.
- Model Denial of Service (DoS): Overloading the model with requests or data can cause disruptions in service and lead to increased costs.
- Supply Chain Vulnerabilities: The reliance on third-party components, services or datasets introduces risks in terms of compromised models, system failures or flawed dependencies.
- Sensitive Information Disclosure: If not properly trained or restricted, LLMs might inadvertently expose confidential information that may lead to legal consequences or loss of competitive advantage.
- Insecure Plugin Design: Poorly designed or insecure plugins can lead to vulnerabilities like code execution in applications leveraging LLMs.
- Excessive Agency: Over-reliance on LLMs for decision-making without human oversight can have unintended consequences.
- Overreliance on AI: Trusting AI without proper human oversight increases risks of errors or biased outcomes.
- Model Theft: Competitors or attackers could reverse-engineer models and steal intellectual property or proprietary technology.
Adopting Best Security Practices: AI TRISM and Beyond
Organizations need a structured approach to secure the use of GenAI models. One framework gaining attention is AI Trust, Risk, and Security Management (AI TRISM), which focuses on managing the entire lifecycle of AI systems— from data security and model monitoring to risk assessment and compliance.
Key AI Security Practices:
- Identify and Catalog Models: Maintain an inventory of all AI models used within the organization, ensuring clear documentation of their purpose and integration points.
- Assess AI Model Risks: Evaluate each model based on criteria like fairness, bias, accuracy, and toxicity. This process also includes tracking the creator, version, and potential vulnerabilities of each model.
- Monitor Data Processing: Implement robust systems to map, monitor, and ensure visibility into data processed by AI models. This is crucial for understanding how data flows through the system and identifying any potential security gaps.
- Implement Data and AI Controls: Securely manage both the data and the AI models, ensuring that proper access controls, encryption, and security protocols are in place to safeguard sensitive information.
- Maintain Regulatory Compliance: Stay aligned with regulatory standards and best practices for AI governance framework, such as those recommended by NIST AI Risk Management.
The Road Ahead: Balancing Innovation and Security
Given GenAI’s vast and undeniable potential, enterprises must prioritize security while simultaneously allowing for enough flexibility to innovate on various models and use cases. Initiatives such as CoSAI, frameworks like AI TRISM, and a clear understanding of emerging threats provide organizations with a solid foundation to build safeguards within their AI environments and implement consistent guidelines that address new challenges.
The future of AI lies not just in its innovation, but in our collective ability to secure it. With vigilance, collaboration, and the right strategies in place, organizations can securely unleash the full potential of GenAI. To learn more about our offerings in Enterprise IT Security and how they can help your organization with your GenAI strategies, reach out to us.
