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GenAI Rollout Blueprint

AI Rollout Blueprint: Secure against AI risks with guardrails (5/5)

Niv Hertz
Niv Hertz

Director of AI

6 min read Jan 01, 2024

Generative AI (GenAI) is rapidly transforming industries, extending capabilities beyond traditional human tasks. This advancement, however, introduces significant new risks that require immediate, expert attention to manage effectively. Ensuring the secure and ethical deployment of GenAI is now more crucial than ever.

In the final article of our AI Rollout Blueprint series, we explore the critical phase: “Safeguard Against Risks” (5/5). Let’s explore the crucial role of AI guardrails in mitigating these risks and ensuring AI’s secure and effective utilization in content marketing.

Check out the previous installments in this series to learn how we got here: Start Here, Think Big, Start Small, and The POC

Objective

The primary objective of this article is to guide organizations through the ongoing evolution of AI products, emphasizing the importance of proactively managing and mitigating risks associated with AI deployment. 

Unravel the complexities of brand protection, compliance adherence, customer satisfaction, and data security as we explore proactive measures in safeguarding enterprise AI.

Understanding risks in AI

Integrating AI into enterprise operations or shipping GenAI apps introduces inherent risks that demand proactive planning and ongoing vigilance. These risks can impact various aspects of business operations and reputation if left unaddressed. 

These risks include:

1. Brand damage

The deployment of AI chatbots carries the risk of generating off-topic, NSFW, or hallucinated responses, potentially sparking public discussions on social media and tarnishing the brand’s image.

For example, Microsoft’s Tay, an AI chatbot on Twitter that aims to interact with customers, began generating controversial and explicit responses, leading to its shutdown within the first 24 hours. This incident tarnished Microsoft’s brand image and underscored a crucial lesson for companies: meticulous design and vigilant monitoring of AI systems are imperative.

2. Compliance violations

The deployment of AI in customer-facing roles poses a critical risk of inadvertently breaching compliance regulations for enterprises. In pursuing enhanced customer interactions, organizations may unintentionally find themselves violating established compliance standards. 

This risk highlights the importance of meticulous planning and continuous vigilance to ensure AI applications align with regulatory standards, such as the GDPR, ISO 27001, etc. Addressing compliance concerns becomes essential as businesses navigate the complex landscape of deploying AI solutions, particularly in customer-centric operations where adherence to regulations is crucial for maintaining trust and avoiding legal repercussions.

3. Negative customer experience

Like in a fraud detection system, AI model drift can lead to false positives in credit card blocks, resulting in a negative customer experience and potential customer loss.

For instance, the Office of the Comptroller of the Currency imposed a $250 million fine on Wells Fargo due to inadequate supervision of its fraud detection system in 2020. The system’s high incidence of false positives resulted in customer dissatisfaction and adversely affected the bank’s standing.

4. Sensitive data leakage

There’s a risk of sensitive information, such as Personally Identifiable Information (PII) or private code, being inadvertently leaked to AI training data and exposed to the public.

In 2023, Microsoft AI researchers inadvertently disclosed 38 terabytes of highly sensitive private data, comprising private keys and passwords, while publishing a storage bucket. This data breach came to light through the efforts of security researchers at Wiz, a cloud-security company. This incident highlights the critical significance of implementing robust security measures and protocols in deploying AI systems.

5. Suboptimal decisions due to AI

Inaccurate AI-driven demand forecasts, caused by model drift, can lead to suboptimal decisions by employees, resulting in substantial financial losses for the organization.

In 2019, Walmart faced a substantial setback, incurring a $1 billion loss attributed to inaccurate inventory forecasting. The root cause was identified in the company’s AI-powered demand forecasting system, which encountered model drift. This deviation led to suboptimal decisions by employees, contributing to a notable decline in stock prices and a significant financial loss for Walmart.

The value of guardrails

In AI, projects are like ‘monsters,’ inherently non-deterministic entities. Understanding this analogy underscores the significance of guardrails. 

Guardrails are essential in steering AI systems away from potential pitfalls and ensuring the responsible application of AI technologies. They serve as proactive measures to manage and mitigate AI hallucinations and other risks associated with GenAI apps, encompassing concerns like brand damage, compliance violations, negative customer experiences, sensitive data leakage, and suboptimal employee decisions influenced by AI. 

Rogue AI can pose real-world risks, from generating off-topic responses to exposing sensitive data. Guardrails become the guiding force ensuring AI technologies’ ethical, responsible, and secure deployment. By delineating clear boundaries and guidelines for AI systems, organizations can significantly reduce the likelihood of undesirable outcomes, maintaining control over the trajectory of their AI applications.

The need for a specialized blueprint for Generative AI applications

A well-crafted service blueprint provides a comprehensive, top-down perspective of the AI application. It explains the connection among various service components, such as individuals, processes, and technology. This encompasses various modules, including but not limited to data pre-processing, model training, and deployment within Gen AI.

A detailed Gen AI blueprint helps identify potential risks, from ethical concerns such as data bias to technical challenges like model drift.

Benefits of a well-designed service blueprint

A meticulously designed service blueprint offers several advantages:

  • Comprehensive Insight: The visual representation explains the interplay between diverse service components, facilitating a thorough grasp of the entire application.
  • Risk Anticipation: GenAI projects often operate as enigmatic ‘black boxes’ with intricate and less accessible internal workings. A detailed service blueprint is a proactive tool to identify potential issues and risks, allowing organizations to address them.

By adhering to our AI Rollout Blueprint series, organizations can effectively advance and secure their AI solutions, mitigating potential risks and ensuring responsible and ethical AI products and apps. 

Unlock confidence in your AI with Aporia Guardrails!

Supercharge your AI deployment with Aporia’s cutting-edge solutions. Mitigate RAG hallucinations, fend off prompt injection attacks, shield against PII data leakage, steer clear of NSFW content mishaps, and fortify defenses against brute-force attacks and bots. 

Trust Aporia to seamlessly put robust guardrails around your AI, ensuring a journey free from the pitfalls of hallucinations, toxicity, data breaches, and more. Elevate your AI strategy—choose Aporia for a secure and reliable AI experience!

Book your demo today!

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