The Pyramid Framework for AI Cybersecyrity Infrastructure

Security in AI systems, particularly Generative AI, cannot be an afterthought. It must be woven into every layer of infrastructure, ensuring robust safeguards against the inherent vulnerabilities in these systems. Tackling security only as a post-implementation step to meet regulatory requirements leaves organizations exposed to evolving threats that traditional methods cannot address. The promise of Generative AI brings transformative opportunities, but with these advancements comes a heightened responsibility. The complexity and scale of these systems demand that security be prioritized as a foundational element. This article is crafted to provide tech leaders and CTOs with actionable insights into the necessity of AI security and practical strategies to champion secure AI practices within their organizations.

Protecting User Data: A Non-Negotiable Priority

A recent study by Gartner revealed that by 2025, 40% of organizations using AI will see compromised results due to neglected security practices. While innovation drives AI adoption at an unprecedented pace, fewer than 30% of AI projects incorporate security considerations during the design phase. This discrepancy exposes organizations to significant risks, from data breaches to regulatory non-compliance, which can undermine the very benefits AI seeks to deliver. The objective of Generative AI (GenAI) security is to safeguard the entire infrastructure, from data ingestion to user interactions, ensuring that every layer of the system remains protected against evolving threats. Therefore, part of the focus must include:

1. Securing Data Ingestion Infrastructure: Validating the legitimacy of incoming data and preventing unauthorized or malicious inputs.
   
   
2. Protecting Data Manipulation Operations: Safeguarding the integrity of data transformations and operations within the system.
   
   
3. Ensuring Model Deployment Security: Defending AI models from unauthorized modifications and malicious access.
   
   
4. Securing User Interactions: Protecting sensitive conversations and ensuring user trust in AI systems.
   
   

The Security Pyramid Framework for Robust Infrastructure

Customer data, including prompt history and AI-generated responses, must be treated as critical and highly sensitive. Conversations with GenAI, while between a human and machine, often reflect personal or proprietary information that demands the same level of security as human-to-human interactions. Any compromise in protecting this data could severely erode user trust and lead to significant reputational and financial damages. Recent discussions have highlighted concerns about the degrading quality of responses from ChatGPT and other GenAI systems, raising questions about potential vulnerabilities.

While OpenAI has refuted claims of declining performance, studies have shown that response quality in AI models can deteriorate over time, often due to model drift or insufficient retraining. This concern grows significantly when considering the risks posed by bad actors. In an internally deployed GenAI system, unauthorized modifications could lead to compromised outputs indistinguishable from expected degradation. Such attacks can undermine system reliability and user trust while exposing critical operational vulnerabilities. While standard cybersecurity practices lay a foundation, AI-specific measures are essential to achieve a robust security posture. Here are some of the most critical risks unique to GenAI systems:

1. Model Manipulation

While it may seem far-fetched, model manipulation is a growing concern highlighted by organizations such as the National Institute of Standards and Technology (NIST). Cyberattacks targeting AI models can exploit vulnerabilities to manipulate their behavior. These include:

• Evasion Attacks: Fooling the AI into misclassifying data.
• Privacy Attacks: Extracting sensitive information from the model’s outputs.

Model manipulation often overlaps with data poisoning, making it difficult to distinguish between the two. To mitigate these risks, organizations must implement robust model monitoring, auditing, and secure deployment practices.

2. Data Poisoning

This is an exploit where hackers inject poisoned data into the training corpus, leading to unexpected or harmful responses. For example, if a company relies on AI for data and signals intelligence, the introduction of tampered, deleted, or maliciously added data could distort analysis results and compromise decision-making. Properly crafted poisoned training data can have a profound impact, producing erroneous outputs or embedding vulnerabilities into the AI model itself. This risk underscores the need for rigorous data validation and monitoring throughout the AI lifecycle.

3. Prompt Injection Attacks

One of the most unique attack vectors in Generative AI is prompt injection, which does not exist in traditional cybersecurity practices. These attacks exploit the natural language processing (NLP) nature of GenAI systems by inserting specially crafted prompts designed to bypass safety controls. For example:

• Unauthorized prompts may lead to the generation of confidential information or unintended responses.
• In worst-case scenarios, attackers could manipulate prompts to expose the model’s inner workings or bypass ethical safeguards.

This issue arises because Generative AI models function largely as black boxes, where their reasoning pathways remain opaque. Ongoing research and input validation techniques are vital to mitigate prompt injection risks.

4. Supply Chain Attacks

The nascent state of Generative AI has driven rapid innovation, often at the expense of supply chain security. AI systems frequently integrate third-party components, libraries, and APIs, each introducing potential vulnerabilities. Malicious or modified components can serve as entry points for attackers. Examples include:

• Compromised libraries embedding backdoors.
• Vulnerabilities introduced by dependencies used in model deployment.

How To Approach The Problem

Generative AI security cannot be addressed with a one-size-fits-all approach. Attempts to standardize security often fall victim to checklist-style compliance, which fails to account for the dynamic and evolving nature of AI systems. Instead, security must be a mindset embedded into the culture of an organization, supported by robust, adaptive practices that evolve alongside technology.

At its core, securing Generative AI infrastructure demands a secure-by-design architecture. This involves principles such as access control, the least privilege model, encryption for data at rest and in transit, and advanced threat and anomaly detection systems. However, these architectural components must also adapt to new research findings and shifting business needs. Without proactive operational reviews and regular security assessments, such changes risk expanding the attack surface. Organizations must foster a culture of openness, where team members feel empowered to discuss potential threats and solutions.

For complex threats such as supply chain attacks, additional measures are required. Digitally signing all components and validating them before deployment is a solid foundation. For greater security, organizations can implement Software Bill of Materials (SBOM) tracking, a practice endorsed by the Cybersecurity and Infrastructure Security Agency (CISA) to ensure full visibility into third-party components. As AI systems integrate more deeply into critical infrastructure, it becomes vital to address their unique security risks. Borrowing from traditional frameworks like the Pyramid of Pain in cybersecurity, the Security Pyramid of AI offers a tiered approach to identifying, evaluating, and mitigating risks associated with AI-driven systems. This framework allows Chief Information Security Officers (CISOs) and security teams to prioritize their efforts based on the complexity and severity of threats.

A Holistic Approach For AI Cybersecurity Infrastructure

Securing Generative AI systems requires a holistic and adaptive strategy, as no single method can address the multifaceted risks inherent to these technologies. Traditional checklist-based compliance approaches often fall short, failing to accommodate the dynamic nature of AI. Instead, security must be ingrained as a mindset and cultural value, fostering proactive collaboration and innovation in threat mitigation.

Secure-by-Design Architecture

At the heart of a robust security framework lies a secure-by-design architecture, which integrates essential safeguards into every layer of Generative AI infrastructure. This foundational approach provides a resilient baseline, enabling organizations to build AI systems that are both secure and scalable.Key components of such an architecture include:

• Access Control: Implementing strict permissions to limit system access to authorized users and applications.
• Least Privilege Model: Ensuring that users and processes only have the minimum access necessary to perform their functions.
• Encryption: Protecting data at rest and in transit through robust cryptographic protocols.
• Threat and Anomaly Detection: Deploying advanced systems to identify and respond to unusual activity in real time.

Evolving Security Culture for Dynamic Architectures

Generative AI architectures are not static; they evolve with advancements in research, changing business requirements, and external pressures. However, these changes can inadvertently expand the attack surface, creating new vulnerabilities. Establishing a culture that encourages teams to proactively review changes and openly discuss vulnerabilities is critical to maintaining a strong security posture. Therefore, to mitigate these risks, organizations should implement:

• Operational Reviews: Regular evaluations of architectural changes to identify potential risks and ensure adherence to security best practices.
• Annual Security Overviews: Comprehensive assessments of the entire infrastructure to evaluate its resilience against emerging threats.

Ultimately, Generative AI security transcends technology and processes—it is a cultural commitment to vigilance, adaptability, and accountability. Organizations that embrace a proactive security culture will be better equipped to navigate the evolving landscape of AI threats, ensuring the integrity, confidentiality, and availability of their systems.

Addressing Supply Chain Threats

Supply chain threats represent a particularly complex challenge in Generative AI. These risks arise from the reliance on third-party components, open-source tools, and pre-trained models, which may introduce vulnerabilities. By integrating these practices, organizations can better secure their AI supply chain, reducing the risk of compromised components undermining their systems. Effective measures to mitigate supply chain risks include:

• Digital Signatures: Signing all components and validating their integrity before deployment.
• Software Bill of Materials (SBOM) Tracking: Maintaining a detailed record of all software components, as recommended by the Cybersecurity and Infrastructure Security Agency (CISA). SBOM tracking enables organizations to identify and address vulnerabilities in third-party libraries and dependencies.

Case Study: AI21 Labs Cybersecurity Breach

In 2023, AI21 Labs, an Israeli company specializing in Natural Language Processing (NLP), faced a significant cybersecurity challenge when a data breach exposed sensitive user information from its AI21 Studio platform. The breach was traced to a vulnerability in the authentication mechanism, which allowed unauthorized access to user accounts and proprietary AI models. This incident compromised the confidentiality of data from several high-profile clients, including educational institutions and content creators. Wikipedia

Investigations revealed that AI21 Labs had not promptly addressed the identified vulnerability despite receiving alerts from independent security researchers. The delay in remediation allowed attackers to exploit the weakness, leading to unauthorized data access and potential intellectual property theft. This oversight highlighted the critical importance of timely responses to security vulnerabilities, especially in companies at the forefront of AI innovation.

In response to the breach, AI21 Labs took several corrective actions to strengthen its security posture. The company overhauled its authentication mechanisms, implementing more robust multi-factor authentication (MFA) protocols to ensure that only authorized users could access sensitive platforms. Additionally, AI21 Labs conducted comprehensive security audits of its systems to identify and address other potential vulnerabilities. The company also established a more responsive protocol for engaging with security researchers, ensuring that future vulnerability reports would be addressed promptly to prevent similar incidents.

This incident underscores the complexities and challenges that companies face when integrating advanced AI technologies into their operations. While AI21 Labs is renowned for its innovative contributions to NLP, the data breach revealed that rapid technological advancement must be accompanied by equally robust security measures. The case illustrates the necessity for organizations to maintain a proactive approach to cybersecurity, especially when developing and deploying cutting-edge AI solutions.

The AI21 Labs breach serves as a cautionary tale for Chief Information Security Officers (CISOs) and other security professionals in the tech industry. It highlights the imperative of integrating security considerations into the development lifecycle of AI technologies, rather than treating them as an afterthought. The incident also emphasizes the importance of fostering a security-conscious culture within organizations, where potential vulnerabilities are promptly addressed, and collaboration with external security experts is encouraged.

Conclusion

The role of a CISO is increasingly complex and the integration of Generative AI adding further complexity to organizational infrastructure. While prioritizing innovation over security might seem appealing, it poses significant risks, including data breaches, regulatory penalties, and eroded customer trust. A culture shift toward proactive security cannot rest solely on CISOs; engineers and program managers must step up as security champions, seizing opportunities to drive change, enhance their careers, and ensure Generative AI's future is both secure and resilient. Organizations that act decisively in this area will be best positioned to navigate emerging threats and thrive in the evolving landscape.

Secure the Future of Your AI Infrastructure Today.
Protect your organization against evolving AI threats with a comprehensive security framework. Contact us to learn how to implement secure-by-design architectures, enhance data protection, and mitigate risks unique to Generative AI systems. Don't leave security to chance—partner with experts who prioritize innovation and resilience.