
Transforming Telco: AI in Telecommunications
Based on our previous work with telcos and our research, we have identified many impactful AI-led use cases.
Security in large language models (LLMs) is crucial and early adoption of the right safety measures is important to protect AI models, as it is with any other advanced form of technology.
The signs are there that while many employees are already embracing the benefits that generative AI can bring, some of the companies they work for are more hesitant and cautious, waiting a little longer before making their move.
A recent McKinsey Global Survey found that employees are far ahead of their companies in using GenAI overall, with only 13% of businesses falling into the early adopter category. Of these early adopters, nearly half of their staff – 43% – are heavy users of GenAI.
Moreover, the IBM Institute for Business Value found that 64% of CEOs are receiving significant pressure from investors to accelerate their adoption of GenAI. But most – 60% – are not yet implementing a consistent, enterprise-wide approach to achieve this.
Why? One key reason appears to be due to considerations around ensuring security, with more IBM data showing that 84% are worried about the risks of any GenAI-related cybersecurity attacks.
In this article, Part 1, we will cover some of the most common potential types of attacks on LLMs, then explain how to mitigate the risks with security measures and safety-first principles.
In the next article, Part 2, we will explore advanced attack techniques against LLMs in detail, then run through how to mitigate these risks using external guardrails and safety procedures.
Research from Security Intelligence has evaluated the potential impact of six main attack types on GenAI, alongside how challenging it is for attackers to execute them:
There are more types of attack to prepare for though.
For a more comprehensive list of threats facing LLMs, the Open Web Application Security Project (OWASP) is a valuable resource. OWASP provides guidelines to help risk management and data security experts prepare for a wide range of different potential technological vulnerabilities.
OWASP’s Top 10 is particularly useful as it provides a hierarchy of the biggest security risks, updated every few years to capture any growing threats.
In this table, cybersecurity experts Lasso detail the OWASP Top 10 potential vulnerabilities for LLMs and different ways to prevent them.
Proactive measures that can help to secure LLMs include regularly auditing user activity logs, with strong access controls and authentication procedures also in place.
Developers need to fine-tune bespoke LLMs for improved accuracy and security before launch. They also need to put measures in place to clean and validate input data before it enters the LLM’s system.
In an AWS Machine Learning blog post, Harel Gal et al outline how AI model producers and the companies that leverage them must work together to ensure that appropriate safety mechanisms are in place.
Responsibility lies with AI model producers to:
Companies deploying LLMs also have a responsibility to ensure that they or their provider have put key security measures in place.
In addition to creating blueprint system prompt template inputs and outputs, these measures include:
We will share more details on these approaches shortly.
Applying these safety mechanisms creates layers of security for LLMs:
Amazon Web Services (AWS) has a wide range of security-related applications well suited to business infrastructure. For context, the below diagram shows the AWS Security Reference Architecture (SRA).
There are three tiers in the SRA workload:
From the below diagram, selected highlights include:
We’ll explore several external guardrails for GenAI applications comprehensively in Part 2 of this article, summarizing key insights from the aforementioned AWS Machine Learning blog post from Harel Gal et al.
These include Amazon Bedrock and Comprehend.
Here at Neurons Lab we follow a comprehensive security framework that covers all bases for successful and secure AI projects. Informed by clients’ priorities around safety, these are some of the highlights:
Veracity
Toxicity & safety
Intellectual property
Data privacy & security
In the next article – Part 2, coming soon – we will explore advanced attack techniques against LLMs in detail, then explain how to mitigate these risks using external guardrails and safety procedures.
Neurons Lab delivers AI transformation services to guide enterprises into the new era of AI. Our approach covers the complete AI spectrum, combining leadership alignment with technology integration to deliver measurable outcomes.
As an AWS Advanced Partner and GenAI competency holder, we have successfully delivered tailored AI solutions to over 100 clients, including Fortune 500 companies and governmental organizations.
Based on our previous work with telcos and our research, we have identified many impactful AI-led use cases.
We explore advanced attack techniques against LLMs, then explain how to mitigate these risks using external AI guardrails and safety procedures.
The recently released SWARM framework offers a simple yet powerful solution for creating an agent orchestration layer. Here is a telco industry example.
Traditional chatbots don't work due to their factual inconsistency and basic conversational skills. Our co-founder and CTO Alex Honchar explains how we use AI agent architecture instead.
The AWS Public Sector Program (PSP) recognizes partners serving government, healthcare, education, space, and non-profit customers.