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Key takeaways:
- Building trust in AI requires a connected approach across engineering, governance, cybersecurity, testing, monitoring, and evaluation, with evidence and oversight embedded throughout the AI lifecycle.
PwC brings together a broad range of capabilities to help organizations build confidence in AI systems, manage evolving risks, and scale adoption in ways that create value.
PwC is a founding member of the AICPA working group behind SOC 2 + AIUC-1 and an early member of the AIUC-1 consortium. The newly announced initiative combines SOC 2 controls attestation and AIUC-1 certification into a single CPA-led engagement and report addressing cybersecurity and AI-specific risks.
The more organizations ask AI to do, the more important it becomes to know when and why they can trust the outcomes. AI is moving deeper into consequential business processes, with agents increasingly able to act with greater autonomy. The act of building trust has moved beyond a governance program and is moving faster than most organizations are prepared to address. Businesses need confidence that AI is secure, reliable, and operating as intended if they are going to rely on it for work that matters.
As AI agents take on more work, building that confidence requires more than any single control, test, evaluation, or human review. AI systems bring together technology, data, people, and processes, and establishing trust in them requires an equally connected approach. AI engineering, governance, controls, cybersecurity, testing, monitoring, remediation, evaluation, and independent assurance all have a role to play, depending on the technology, the risks, and the business outcome at stake.
PwC brings together a distinctive depth and breadth of capabilities to help organizations build trust in AI, reflecting our longstanding role in helping businesses navigate complex questions with confidence. That multidisciplinary approach allows the firm to address trust throughout the AI lifecycle, from how systems are designed and governed to how they are secured, tested, evaluated, and monitored once in use. We help organizations build the confidence to adopt AI, rely on it for increasingly meaningful work, and scale it in ways that create value.
Match the level of confidence to what is at stake
As AI is used in far more consequential ways, new solutions are required to foster trust. But not every AI system presents the same risk, and not every organization needs the same form of confidence.
An AI system involved in a consequential financial, operational, or customer decision may warrant a considerably different level of engineering, testing, monitoring, and evaluation than a lower-risk productivity tool. The appropriate rigor should reflect the significance of the activity, the autonomy of the technology, and the potential consequences if something goes wrong.
The same is true of independent assurance. Traditional approaches to building and communicating confidence in systems are evolving to meet these needs. Companies seeking a transparent, independent assessment of their controls and external evaluation of agentic systems can look to leverage the newly announced SOC 2 + AIUC-1 initiative, which combines SOC 2 controls attestation and AIUC-1 certification into a single CPA-led engagement and report addressing cybersecurity and AI-specific risks. PwC is a founding member of the AICPA working group behind the initiative and an early member of the AIUC-1 consortium, helping shape the report and related practitioner guidance as organizations seek more integrated approaches to evaluating AI systems.
PwC has a long history of delivering this kind of assurance. With the demands for trust in AI growing, we are committed to delivering the services that provide organizations this kind of transparency for clients today.
Build trust as part of the AI lifecycle
Leading AI capabilities change what organizations need to establish trust. Models operate across large technology environments, interact with significant volumes of data, and increasingly connect with other systems and agents. As those systems become more dynamic and autonomous, traditional approaches centered on point-in-time reviews or reconstructing evidence after the fact become increasingly difficult to apply.
The remedy: establish trust as part of the design. Building trust starts well before an AI system is put into use and continues well after.
That can include repeatable and traceable processes during development and deployment, rigorous evaluation, testing, and execution of clearly defined controls before launch, ongoing monitoring of both AI and human behavior once a system is operating, clear escalation pathways and incident-management processes, and what is often missing—appropriate retention of required evidence and plan for transparency through each step.
Creating trust in AI therefore requires orchestrating multiple disciplines across the lifecycle. Engineering, governance, cybersecurity, evaluation, testing, and monitoring each address different parts of the challenge and work together to build confidence in how AI operates. Evaluation can help organizations understand whether systems are performing as intended, while independent assurance can provide an additional level of confidence when stakeholders need an outside view. Both require evidence, and increasingly, should be part of how organizations back up the claims they provide to users, customers, employees, and others.
One size does not fit all. The appropriate combination of techniques, level of rigor, and approach depend on what the AI is doing, the risks involved, the quality levels required, and who ultimately depends on the outcomes.
Build the evidence as AI operates
One of the most important shifts is how organizations think about evidence.
Increasingly autonomous AI creates a dynamic environment. Models change. Data changes. Tools and systems interact in new ways. Agents may take actions at speeds and volumes that make reconstructing every meaningful decision months later impractical. Modern processes require a new type of interaction between agents and humans with new mechanisms for evidencing action and accountability.
The opportunity is to design AI so that useful evidence is generated as the system is built, deployed, and operated.
That might mean retaining evidence of what an agent was authorized to do, what actions it took, what information it relied upon, where exceptions occurred, and when and how a person intervened. As the volume of that evidence grows, organizations also need the ability to analyze it effectively, surface meaningful exceptions, and identify where new or increasing risks may require attention.
Done well, that evidence can serve more than a control function. It can help leaders understand performance, give boards greater visibility, and provide customers, counterparties, or regulators with greater confidence in how AI is being used.
In that sense, demonstrable trust becomes part of the value proposition itself.
Trust can help unlock the next stage of AI adoption
For generations, PwC has helped organizations build confidence in complex systems, processes, and information. We have long been part of the structures that build confidence in complex ecosystems—from the capital markets to cloud and digital safety. AI brings new technical complexity to the challenge, but the underlying objective is familiar: creating the evidence, transparency, and accountability that allow people to rely on systems they cannot necessarily see or understand in full. Leverage the right techniques for trust at each stage of build and as transparency strategies are formed.
As AI takes on more consequential work, organizations that can demonstrate how their systems are built, governed, and monitored will likely be better positioned to earn confidence from the people who rely on them. Trust becomes part of the product—creating the foundation that enables AI that is adopted, scaled, and delivering value.
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