When companies adopt generative AI, they anticipate a range of advantages from these initiatives—such as increased efficiency and productivity, faster business operations, and greater innovation in products and services. Nonetheless, a fundamental aspect of this AI evolution is confidence. Trustworthy AI depends on understanding the functioning and decision-making processes of the AI.
As per a study conducted with C-suite executives from the IBM Institute for Business Value, 82% of respondents emphasize that ensuring secure and reliable AI is pivotal for the success of their business. However, only 24% of current generative AI projects are being safeguarded, leading to a significant gap in securing known AI projects. Furthermore, the existence of ‘Shadow AI’ within organizations exacerbates the security void for AI.
Issues Encountered in Securing AI Implementation
Organizations are now embarking on a slew of projects that harness generative AI. During the phase of data acquisition and handling, copious amounts of data must be amassed to feed the model, granting access to various individuals like data scientists, engineers, developers, and others. This, by its nature, poses a risk by consolidating all that data in one location and providing multiple users access to it. Consequently, generative AI acts as a novel data repository that is capable of generating new data derived from existing organizational data. Be it through model training, fine-tuning, or connection to a RAG (Vector DB), that data likely contains PII, privacy issues, and other sensitive details. This accumulation of sensitive data serves as an enticing target for malicious actors seeking unauthorized entry.
In the realm of model development, new applications are being constructed in an innovative manner, yielding fresh vulnerabilities that serve as potential gateways for exploitation by attackers. Development typically commences with data science teams downloading and repurposing pre-trained open-source machine learning models from online model repositories such as HuggingFace or TensorFlow Hub. These open-source model-sharing platforms have evolved from the innate complexities of data science, scarcity of practitioners, and the value they confer to organizations by significantly reducing the time and effort required for adopting generative AI. However, such repositories might lack comprehensive security measures, thereby shifting the risk burden onto enterprises—and malicious entities are eager to capitalize on this opportunity. They might embed a backdoor or malware into one of these models and subsequently upload the infected model back to the model-sharing repositories, endangering anyone who downloads it. The overall scarcity of security surrounding ML models, along with the increasing sensitivity of data that these models encounter, amplifies the potential damage of attacks targeting these models.
During inference and live usage, attackers could tamper with prompts to bypass safeguards and coerce models into malfunctions by producing prohibited responses laden with bias, falsehoods, and other detrimental content, thereby tarnishing the reputation. Alternatively, attackers could meddle with the model and scrutinize input-output pairs to create a surrogate model that emulates the behavior of the target model, effectively usurping its functionalities, thereby depriving the enterprise of its competitive edge.
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Essential Measures for Securing AI
Various organizations are adopting diverse strategies to secure AI as the protocols and frameworks for AI security continue to evolve. IBM’s approach to securing AI revolves around safeguarding the core elements of an AI deployment—such as protecting the data, fortifying the model, and ensuring secure usage. Additionally, the infrastructure on which AI models are created and operated must be secured. Organizations must also establish AI governance, overseeing fairness, bias, and drift over time, all in a continuous fashion to monitor any alterations or model drift.
- Securing the data: Organizations must centralize and aggregate vast volumes of data to extract maximum value from generative AI. When you centralize your invaluable assets in one location, you expose yourself to significant risks, necessitating a comprehensive data security plan to identify and shield sensitive data.
- Securing the model: Many organizations are resorting to downloading models from open sources to expedite their development endeavors. Data scientists acquire these opaque models without insights into their functioning. Malicious actors also have access to these online model repositories and could introduce a backdoor or malware into a model, uploading it back to the repository as a potential entry point for unsuspecting users. It is essential to comprehend the vulnerabilities and misconfigurations in model deployment.
- Secure the usage: Organizations should ensure the safe utilization of AI deployments. Threat actors might engage in a prompt injection, employing malicious prompts to bypass security measures, gain unauthorized access, pilfer sensitive data, or introduce biases into outputs. Attackers may also devise inputs to collect model outputs, amassing a significant dataset of input-output pairs to train a surrogate model that mimics the target model’s behaviors, effectively “stealing” its capabilities. Understanding the model’s usage and aligning it with assessment frameworks is necessary to ensure safe application.
All these efforts must be undertaken while adhering to regulatory compliance.
Introduction of IBM Guardium AI Security
With the escalating menace posed by existing threats and the growing impact of data breaches, fortifying AI is poised to become a significant undertaking—one where many organizations will require assistance. To aid companies in leveraging secure and reliable AI, IBM has unveiled IBM Guardium AI Security. Drawing from decades of experience in data security through IBM Guardium, this novel offering empowers organizations to safeguard their AI deployments.
This solution facilitates the management of security risks and vulnerabilities associated with sensitive AI data and models. It assists in identifying and rectifying model vulnerabilities and protecting sensitive data. Continuous monitoring is provided for AI misconfigurations, detection of data leaks, and optimization of access control—with a trusted leader in data security.
Part of this novel offering is the IBM Guardium Data Security Center, empowering security and AI teams to collaborate across the organization through integrated workflows, a consolidated view of data assets, and centralized compliance policies.
Safeguarding AI is a continuous endeavor that necessitates collaboration across multifunctional teams—comprising security, risk and compliance, and AI teams—and organizations must adopt a structured approach to secure their AI deployments.
Explore how Guardium AI Security could benefit your organization, and register for our upcoming webinar to delve deeper into the topic.