Home » Uncategorized

Securing your AI data pipeline with MLOps

  • Colin Priest 
A graphic depiction of a data pipeline with arrows flowing in different directions, illustrating the continuous movement and integration of data for real-time insights.  Generative AI technology.

By Colin Priest, Chief Evangelist at FeatureByte

Enterprises are increasingly implementing Artificial Intelligence (AI) into their operations. However, AI-ready data pipeline practices are still in their infancy, especially when it comes to IT security.

The pervasiveness of “Spaghetti Code”

Enterprises delving into AI data pipelines often find themselves wading through a mess of complex and convoluted code, commonly referred to as “spaghetti code.” This jumbled mass is not only challenging to understand but also hard to maintain, while introducing a multitude of security risks.

Due to its intertwined structure, spaghetti code can be incredibly challenging to audit for vulnerabilities. Without clear pathways and logical sequences, potential security flaws remain hidden, making the system susceptible to breaches.

Often, due to its patchwork origins, spaghetti code lacks a uniform structure that aligns with standard security protocols. This inconsistency can lead to unintentional loopholes or backdoors that malicious actors can exploit.

The role of generative AI in code creation

With the advent of generative AI, a small but rapidly growing percentage of this code is now written by AIs. Rather than being trained on high-quality, enterprise-grade code, AIs are learning from publicly available code snippets that do not always prioritize code efficiency or security. The result? AI-generated code may lack the robustness and/or the security enterprises desperately need. While some snippets might be secure and efficient, others could be outdated or riddled with vulnerabilities, inadvertently introducing weak points into the system.

Industrializing code

The solution to this challenge may lie in the realm of industrialized code, which is designed to be secure, traceable, and reusable. Enterprises can gain enormous efficiencies and reliability by switching from tangled spaghetti code to streamlined solutions using standard components.

Industrialized code is created with rigorous checks and balances, ensuring that common pitfalls and errors in coding are avoided. By reducing the chances of human error, a major cause of security vulnerabilities, the overall security posture of applications improves.

Industrialized coding practices come with documentation standards, making it easier for teams to collaborate and understand the codebase. This transparency ensures that any potential security concerns are easier to spot and rectify by any team member, not just the original author. This transparency makes it easier to audit, review, and rectify any potential security issues.

Governance in AI data pipelines: A pressing concern

MLOps have greatly refined validation and deployment practices for machine learning models from their training phase to their deployment. However, AI data pipeline governance practices remain immature.

A common concern is the capacity for arbitrary code execution. This ability, intended for flexibility, inadvertently paves the way for security lapses. The absence of Role-Based Access Controls (RBAC) exacerbates the issue, leaving room for unauthorized access and potential system disruptions.

As a result of lax access controls and the lack of validation checks, AI data pipelines resemble open floodgates. Without stringent validations, there’s a risk of allowing erroneous or malicious code into the system. Such intrusions could distort model outputs, leading to inaccurate results or, even worse, significant security breaches.

Building safer AI data pipelines

To ensure the effective and safe use of AI data pipelines, enterprises need to incorporate three key features:

  1. Code Standardization: By standardizing code and using tools that implement pipelines in a more standard and automated way, you will reduce human error and code maintenance challenges, while also improving security.
  2. Guardrails: Just as they sound, guardrails will keep AI data pipelines on track, ensuring that they operate within specified parameters and don’t go off the rails with unexpected or undesirable outputs.
  3. Role-Based Access Control (RBAC): RBAC ensures that only authorized personnel have access to specific parts of the pipeline. By controlling who can access what, enterprises can significantly reduce the risk of human-induced errors or security breaches.
  4. Governance Processes: This involves a structured process to oversee and manage the AI data pipelines. With proper governance, enterprises can track the versions of LLMs in use, their specific applications, and any potential issues or vulnerabilities.

As the AI ecosystem evolves, enterprises are facing an intricate challenge: crafting robust AI data pipelines that prioritize security, efficiency, and governance. The existing quagmire of spaghetti code, coupled with the risky integration of AI-generated code, underscores a need for change. By embracing industrialized code and embedding stringent governance measures, businesses can navigate the complex AI landscape with increased confidence. In focusing on these foundational aspects, enterprises can not only ensure the effectiveness of their AI systems, but also safeguard their operations against potential threats, allowing AI to be both valuable and secure.


Author bio:

<a></a>Securing your AI data pipeline with MLOps

Colin Priest is Chief Evangelist at FeatureByte. With a focus on data science initiatives, he has held several CEO and general management roles, while also serving as a business consultant, data scientist, thought leader, behavioral scientist, and educator. He has over 30 years of experience across various industries, including finance, healthcare, security, oil and gas, government, telecommunications, and marketing.