Trust in the Era of Generative AI: Lessons from the Sydney Harbour Bridge

Looking across the span of the Sydney Harbour Bridge—with its iconic steel arches, massive pylons, and its more than one hundred thousand daily commuters—calls to mind the tremendous amount of trust built into the structure’s very foundation. To make such a complex construction successful and sustainable demands strategic planning, diligent maintenance, accountability, collaboration, and careful risk management.

In the era of generative artificial intelligence (AI), the same principles apply to your data.

With astounding speed, generative AI has leaped into our digital lives and workplaces. But how can we instill confidence in the effectiveness and reliability of this emerging technology? How can we be sure to uphold ethical standards, respect privacy, and operate within the bounds of accountability? In short, how can we ensure trust in AI?

Just as the framework of the Sydney Harbour Bridge has inspired the trust of travelers for over 90 years, so must the framework we build for generative AI inspire trust in the technology’s users. And just as the Sydney Harbour Bridge required a thoughtful plan and innovative solutions, an IT leader’s approach to generative AI requires strategic planning and whip-smart innovation.

The threat, as the Deloitte AI Institute points out, is that generative AI “accelerates and amplifies risks that have always been a factor in AI development and deployment.”

A recent global report confirms that these concerns are top of mind for IT and data decision-makers. The study, Capitalising on Generative Artificial Intelligence, was conducted by research firm Vanson Bourne and sponsored by enterprise information management company Iron Mountain.

Of the 700 respondents, 93% report their organisations are already using generative AI. The study identified key challenges that are keeping those IT and data decision-makers up at night:

• Sourcing, protecting, and preparing data
• Protecting and managing AI-generated assets
• Creating and enforcing generative AI policies
• Complying with AI-related regulations and guidelines

To safeguard the success of generative AI use cases, 96% of those surveyed agreed on the need for a unified asset strategy.

What is a unified asset strategy? It is a comprehensive framework for lifecycle management and protection of all assets at scale. When organisations adopt a unified asset strategy, they accelerate AI innovation, drive value creation, and significantly lower risk. Those with a unified asset strategy in place infuse trust in generative AI in four essential ways.

1. Sourcing, protecting, and preparing data: Trust begins with data. Generative AI models consume massive amounts of physical and digital data that must be safeguarded through anonymisation and encryption. Data must also be free from biases that could skew outputs. Organisations succeed by following rigorous processes designed to maintain data quality, privacy, and relevance.

A unified asset strategy helps by creating a framework for these processes. Physical assets are digitised and enriched with metadata that make it easier to discover and access data. Valuable information can be extracted from unstructured data, and data is protected against unauthorised access.

2. Protecting and managing AI-generated assets: AI-generated text, images, audio, and other assets must be handled with the same rigor and ethical considerations as any digital asset. This means assets should be ethically sourced, securely stored, and accurately attributed. AI-generated content that closely mimics human-created content requires special care. Organisations succeed by establishing protocols to cover all concerns, including those related to copyright and intellectual property.

A unified asset strategy helps by supporting protocols for storage, security, and lifecycle management to keep assets safe and compliant with external regulations and internal policies.

3. Creating and enforcing generative AI policies: In an environment of trust, integrity is continuously earned and reaffirmed. This can be achieved via policies that govern the use of AI and draw clear lines of accountability. Organisations succeed by enforcing AI policies through regular audits, transparency reports, and open channels of communication.

A unified asset strategy helps by aligning AI practices with organisational goals and by providing information governance to back policy creation and enforcement.

4. Complying with AI-related regulations and guidelines: Regulatory compliance and adherence to nascent AI standards give organisations the imperative to trust but verify—with guidelines for data protection, user privacy, and ethical considerations. Organisations succeed by adhering to these guidelines, reassuring users and stakeholders that the AI systems are functioning within legal and ethical boundaries.

A unified asset strategy helps by keeping pace with evolving regulations and policies and by protecting and managing assets to stay in compliance.

A trusted framework

Once in place, a unified asset strategy bolsters collaboration among many stakeholders, including those within organisations—plus AI developers, policymakers, ethicists, and the public. It encourages transparency, ethical responsibility, open dialogue, and continuous vigilance around data integrity and asset management.

At the same time, the framework empowers enterprises to reduce risk while realising more value from their digital and physical assets. Perhaps most crucial, the unified asset strategy, like Australia’s most iconic bridge, becomes a conduit and catalyst for growth.

To Learn More

Dive deeper into the research by reading our research report and infographic. For a broader look at how a unified asset strategy can help optimise your digital future, visit our resource page.