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Build confidence in AI so you can move faster—safely

Trust in AI 

Artificial intelligence (AI) is transforming businesses by driving smarter decisions, automating complex tasks, and unlocking new sources of value and innovation at unprecedented speed and scale. As organizations deploy increasingly powerful AI systems, the risks become more complex, multi-dimensional, and harder to manage. 

Stepping back isn’t an option. What matters now is building and executing a strategy that embraces AI-driven transformation while protecting value, delivering return on investment, and managing new and emerging risks. 

Responsible AI is a set of practices that builds trust with AI initiatives. Responsible AI creates confidence in decisions, balancing the risks and rewards of adopting AI technologies and solutions. Risk is more effectively managed through standardized measurement and evaluation of risks, governance, controls commensurate with risks, and tools to monitor performance over time. The result? Reduced operational friction, clearer roles, faster time to deployment, and sustained value capture from AI investments—with fairness, transparency, privacy, security, and resilience built in.

Solutions that reduce time to value

 

How we help

At PwC Canada, we help organizations move from AI ambition to trusted AI outcomes. Our approach is practical, collaborative, and tailored to your business needs—so you can innovate confidently, accelerate value, and manage risk at every stage.

Organizations are adopting AI across the enterprise. Leaders must not only govern and secure, but also prove AI’s fairness, robustness, and compliance through independent assurance.

Assess your baseline

Evaluate if your current processes, policies, and operating model reflect responsible AI leading practices and align with your organization’s AI ambitions:

  • AI legal or regulatory readiness assessment
  • Responsible AI maturity assessment
  • AI impact assessment

Foundational capabilities

Set the foundation for your program with these foundational capabilities:

  • Responsible AI principles
  • AI use case inventory
  • AI risk taxonomy
  • AI risk intake and tiering

Governance and operating model design

Operationalize foundational capabilities through an accountability—and communication—structure that sets your organization up for success:

  • Operating model roles and responsibilities
  • Governance committees and escalation paths
  • AI risk and control matrix
  • Training and communication

Trust by design

Establish processes, standards, and testing to build perpetual trust and transparency in your implementations:

  • AI development and deployment standards
  • AI testing and monitoring (including model testing and red teaming)
  • Risk mitigation tracking and reporting

Scaling with confidence

Scale and evolve your risk management functions to keep pace as you expand AI initiatives. To operationalize responsible AI effectively, these key functions will be essential:

  • Internal audit
  • Cybersecurity
  • Data governance
  • Compliance and legal
  • Regulatory readiness
  • Data risk and privacy

AI assurance

Validate and demonstrate the trustworthiness of your AI systems. We can support with:

  • Independent assurance, audit, and attestation for AI systems
  • AI model validation (fairness, robustness, drift, performance)
  • Readiness for regulatory assurance (e.g. ISO 42001, American Institute of Certified Public Accountants (AICPA))
  • Non-financial assurance for AI governance, controls, and transparency
  • Ongoing managed validation of models in production

Trust by design across the life cycle

Our Trust in AI services are delivered through a combination of expert consulting, managed services, and proprietary tools. Our approach is practical, collaborative, and tailored to your business needs—so you can innovate confidently, accelerate value, and manage risk at every stage.

Set the direction and guardrails for responsible AI, ensuring alignment with business strategy and regulatory requirements.

  • Responsible AI strategy and planning aligned to your business goals, values, and risk appetite
  • Responsible AI policies and standards to guide decision making, controls, and accountability
  • AI governance roles, responsibilities, and escalation paths for AI oversight
  • AI risk management (e.g. third-party risk management, risk and controls framework)
  • Metrics and reporting to demonstrate responsible AI practices to stakeholders

Protect AI systems and data through robust privacy, security, and incident response mechanisms tailored to the unique risks of AI.

  • AI red teaming and threat modelling
  • AI security and privacy assessments and compliance reporting
  • AI discovery, inventory, and runtime protection
  • Privacy, threat risk, and AI impact assessments
  • Incident response and crisis management

Evaluate and validate AI systems for compliance, performance, fairness, security, and ethical alignment. Demonstrate that AI is trustworthy through independent validation and assurance.

  • Independent assurance, audit, and certification of AI systems, aligned to industry and regulatory standards
  • Model validation

Discover how Canadian organizations are building trust in AI

Explore PwC Canada’s Trust in AI report for actionable insights on priorities, investments, and readiness for trustworthy AI—including agentic AI—across industries.

Follow PwC Canada

Contact us

​Jordan  Prokopy

​Jordan Prokopy

National Data Trust & Privacy Practice Leader, PwC Canada

Tel: +1 647-822-6101

Brenda Vethanayagam

Brenda Vethanayagam

AI Trust Leader, PwC Canada

Tel: +1 416 815 5228

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