AI Systems Are Only as Strong as the Data Behind Them; Here’s How to Protect It

As artificial intelligence (AI) continues to revolutionise industries, understanding the AI system lifecycle becomes vital for both government and business sectors. The lifecycle encompasses the stages AI systems go through; from planning and design to deployment and ongoing monitoring and each stage presents unique risks, particularly around data security.

To help organisations navigate these complexities, a joint advisory was released by leading international cybersecurity entities, including the Australian Signals Directorate (ASD), the National Security Agency (NSA), the Federal Bureau of Investigation (FBI), and others. In their publication it provides guidance on securing data throughout the entire AI system lifecycle, emphasising the importance of data security in maintaining the accuracy and integrity of AI outcomes.  

What Does the AI System Lifecycle Mean for Government and Business?

For government agencies and businesses alike, the AI system lifecycle represents the entire process of creating, deploying and managing AI systems. It’s not just about ensuring that AI technology performs as expected but also safeguarding the data that powers these systems. Poorly managed data can lead to compromised outcomes, biased results, and, in the worst case, security breaches. This is why understanding each phase of the lifecycle is so critical.

1. Plan & Design: At this stage, the AI system is in its conceptual phase. It’s all about laying the foundation for secure AI system development. For businesses and government bodies, this means integrating robust data security measures from the start, including privacy-by-design principles, threat modelling and creating secure protocols for data handling. It’s also essential to make sure that the AI you select will be explainable, transparent, and contestable if the use could be considered high-risk.  

2. Collect & Process Data: Data is the lifeblood of AI. During this phase, organisations must ensure that the data used to train AI models is of high quality, free from malicious influences, and legally compliant. For government and business sectors, this is a high-risk area where poor data management can lead to inaccuracies, breaches, or biased outcomes. It is important to make sure that the data you use is current and clean. Classification, appraisal and disposal before training is a vital step in achieving a quality outcome.  

3. Build & Use Model: With the data collected, the AI model is built and tested. It’s crucial to secure the AI training environment and ensure the data is protected from tampering. Government agencies, especially those dealing with sensitive or critical infrastructure data, need to prioritise this phase to prevent adversarial attacks that could compromise the model’s accuracy and intent. It’s also essential at this stage to make sure that any privacy implications and consents agreed for training use are not being undermined.  

4. Verify & Validate: Security and integrity testing must be conducted during this phase. Both government and business organisations must conduct thorough audits, ensuring that the model works as expected without any unintended flaws, ideally involving external auditors. Verifying data provenance and ensuring that the AI system is not compromised by malicious inputs is key here.

5. Deploy & Use: Once the system is live, it's vital to ensure that it remains secure throughout its operation. For governments, usage could involve managing critical infrastructure or important social welfare outcomes, while businesses may be using AI for decision-making, customer service, or product development, for example. Whatever the use case, transparency is essential. Organisation using AI need to be able to explain and share the manner of its use, including how decisions are made and the data that drives them.

6. Operate & Monitor: AI systems are not set-and-forget; they require continuous evaluation and adaptation. Data drift, where AI’s model performance deteriorates over time as the data it operates on evolves, must be carefully monitored. Businesses and government agencies must be prepared to update their systems regularly and perform continuous risk assessments to safeguard data and model accuracy. Continuous monitoring and zero-trust architecture should be in place to guard against new vulnerabilities being introduced and exploited. Ongoing evaluation of ethics and explainability is key, as closed-box models do adapt and change over time.

Best Practices to Secure Data Throughout the AI Lifecycle

Now that you know the typical lifecycle, here are some tips to achieve success.  

1. Data Provenance Tracking: Ensuring data integrity is paramount. By tracking the origin and journey of data throughout the AI system, businesses and government agencies can identify potential security threats, such as poisoned data or compromised sources, and can make sure privacy and security are not undermined.

2. Data Encryption: To protect sensitive data, robust encryption methods should be implemented at every stage whether data is at rest, in transit, or in processing. This is a critical measure to prevent unauthorized access and ensure data remains protected. Knowing which data is most sensitive using effective autoclassification helps to target the security controls in the right place and at the right level.  

3. Use of Digital Signatures: Digital signatures play a vital role in verifying the authenticity of the data used in AI models. By ensuring that only trusted, verified data is processed, businesses and government entities can safeguard against data tampering or malicious alterations.

4. Secure Storage: As AI systems generate vast amounts of data, secure storage solutions are essential. Storing data in certified systems that adhere to recognised security standards ensures that critical data remains protected from breaches or unauthorised access, and use of certified platforms makes you are more defensible in the event of any issues.

For both government and business organisations, the AI system lifecycle is a critical framework for managing the data security risks associated with artificial intelligence. From the planning stage through to monitoring and adapting AI models, securing data throughout the lifecycle is essential for maintaining the integrity of AI outcomes. By adhering to best practices such as encryption, digital signatures and provenance tracking, organisations can mitigate risks and ensure that AI systems continue to operate securely and effectively.

In an era of rapid technological advancements, proactive data security is not just a regulatory requirement it's a fundamental part of ensuring that AI can be trusted to deliver reliable and accurate results.

View the ASD publication here to explore best practices for securing AI data, and to see how Castlepoint can support you, contact us.

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