Artificial Intelligence
RISK ASSESSMENTS
Innovate Responsibly with Confidence
What is AI Risk?
Artificial Intelligence (AI) risk refers to the potential negative impacts associated with the development and deployment of AI technologies. As organizations like yours race to adopt tools like generative AI and machine learning algorithms, your team could inadvertently overlook the unique vulnerabilities these systems introduce.
AI risk management is the practice of identifying, analyzing, and mitigating these threats. It ensures that your adoption of AI drives innovation without compromising your data privacy, reputation, or compliance standing.
Why You Must Address AI Risk
AI is a powerful tool, but without proper guardrails, it can quickly become a liability.
Addressing AI risk means ensuring your systems operate ethically, protecting sensitive company data from being exposed to public AI models, maintaining compliance with evolving regulations, and building trust by using advanced technology in a responsible and transparent way.
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Enhance Productivity Without Burdening Your Team
Integrating process improvement into an already busy schedule may seem daunting, but our streamlined approach ensures minimal disruption.
Our Green Belt Certified team specializes in facilitating interactive learning experiences that drive engagement to uncover targeted solutions that seamlessly align with your existing operations.
Together, we’ll systematically identify and eliminate waste, streamline workflows, and optimize resources. By focusing on your core processes and making even the smallest improvements, your organization will unlock dramatic results for the long run.
Common AI Risks
The risks associated with AI are distinct from traditional IT risks.
- Algorithmic Bias: The risk that an AI model produces prejudiced results due to flawed training data, potentially leading to discrimination in hiring, lending, or healthcare.
- Data Privacy & Leakage: The danger of employees entering confidential client data or proprietary code into public generative AI tools, effectively exposing it to external parties.
- Security Vulnerabilities: Risks such as “model poisoning” or “prompt injection,” where bad actors manipulate AI behavior to bypass security controls or extract data.
- Accuracy & “Hallucinations”: The tendency for generative AI to confidently present false information as fact, which can lead to disastrous business decisions if unchecked.
Assessment & Inventory
Map where and how AI is currently being used within your organization—sometimes revealing “shadow AI” usage that leadership is unaware of.
Governance Framework
Design policies and ethical guidelines that define acceptable use, oversight, and human-in-the-loop accountability for AI tools.
Validation & Testing
Evaluate your models and third-party tools for bias, accuracy, and security vulnerabilities before full deployment.
Continuous Monitoring
Establish protocols to audit AI outputs regularly, ensuring models remain accurate, fair, and secure over time.
For best results, we encourage your team to include change management best practices in your AI risk management plan. PBMares provides guidance for effective, customized change management techniques tailored to the unique context of AI. The result is a more cohesive, sustainable integration of AI governance and adoption.
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Antonina McAvoy
CISA, CISM, QSA, PCIP
Partner, Risk Advisory Services
Norfolk
Bronach Branan
CPA, ACMA, CLSSGB
Partner, Risk Advisory Services
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