13 Mar 2024

Ensuring responsible AI: A Security and governance guide

The rapid development of AI tools and solutions is creating great opportunity for the way we work and maximise the potential of data. However, as we introduce this new technology into more of our processes and ways of working, how are we protecting ourselves from malicious attacks? Who’s responsible for AI’s choices and recommendations? With more questions being raised on how AI uses data and how we can ensure fair, ethical decisions, we discuss the crucial questions shaping a responsible future for AI.  


The battle for AI security 

While the opportunity that comes with the introduction and development of AI is great, security concerns are front of mind. AI systems can be manipulated by data tampering or vulnerabilities could be targeted. To mitigate these risks, robust security measures to cover the full tool lifecycle are crucial. This can include ensuring your data used to train your AI system is diverse and high-quality, encompassing a wide range of scenarios, demographics, and situations; undertaking thorough security testing throughout development; and continuously monitoring your system for potential threats. Through security best practices, we can continue to reap the benefits of AI, without compromising safety and trust. 


Building trustworthy AI through effective governance 

As AI develops at such a fast rate, there are growing complexities over setting its governance structures in place. For the tools and solutions to be used fairly and ethically, bias and potential discrimination need to be considered. Alongside this, transparency and accountability in understanding how AI systems make decisions and who is assigned to be responsible for their actions is a crucial governance concern.   

Governments and businesses need to work together for overall AI governance to be effective. Governments can establish regulatory frameworks that promote fairness, transparency, and accountability, while businesses must adopt ethical development practices and robust data management strategies.  


Ensuring transparency in AI decision-making 

To be able to trust AI systems requires you to understand how they make their decisions. This understanding allows: 

  • Accountability: When we know how AI decisions are made, we can identify and address potential biases or errors. 
  • Transparency: Understanding the reasoning behind AI actions increases public trust and confidence in its use. 
  • Improvement: We can only identify areas for improvement and performance enhancement for AI tools if we understand how it works.

While AI systems are complex technologies, several methods exist to help achieve transparency, including developing AI technique breakdowns that explain the decision-making process as understandable steps, and providing clear user descriptions and documentation for AI outputs. 


Accountability in the age of AI 

Once developers have designed and trained teams on a particular AI tool, working through a clear legal framework and transparent settings can help identify who is responsible for specific outcomes. Alongside this, a strong company culture of ethical and responsible use can help mitigate risks.  

As AI technologies are still developing and regulations around use are still being worked through around the world, ensuring open collaboration and conversation between stakeholders can help balance the innovation with the accountability.   


Managing the sources for responsible AI 

An AI system will only perform as well as the data it uses. High-quality data is key to training accurate and reliable models, so you need to follow best practice for data management, from regular cleaning, verification, and monitoring. 

There are differences to be aware of between responsible AI practices required for Generative AI and Machine Learning. For example, with Machine Learning, the data you use for training needs to be very high quality to ensure accurate predictions, while Generative AI (Chat GPT etc.) is pre-trained so has different guardrails. So, it’s important that we ground Generative AI large language models using retrieval augmented generation to help mitigate hallucinations and inaccuracy.  

Managing the sheer amount of data required for training AI can be overwhelming, but Microsoft’s cloud-based solutions like Azure Cognitive Services prioritise security, compliance, and ethical considerations. Additionally, the potential for in-house deployments using secured platforms like Microsoft Azure to run different models like GPT-3.5 available in ChatGPT and GPT-4-Turbo from OpenAI allows companies to maintain greater control over data privacy and security. 

AI has incredible potential, but security and governance concerns are holding us back. Ensuring responsible development with high quality data, transparent systems and clear legal frameworks, we can help minimise these challenges and make the most of the opportunities that AI brings.  


“In the Era of AI, it is imperative that we put the right guardrails in place to ensure our AI solutions operate fairly, securely and without bias. At Coeo, we are dedicated to harnessing the transformative power of AI to drive operational efficiency, business value and new opportunities for growth whilst following responsible AI frameworks.”

Nihal Mushtaq Amin, AI Capability Lead