AI Risk Assessment

AI Risk Assessment: Quantify and Manage AI Risks Before They Materialize

An AI risk assessment evaluates the potential risks associated with deploying or using artificial intelligence systems, including bias, safety, security, privacy, compliance, and operational risks. Petronella Technology Group performs comprehensive AI risk assessments aligned with NIST AI Risk Management Framework (AI 100-1) and EU AI Act requirements, helping organizations deploy AI responsibly while meeting regulatory obligations. Combining 24+ years of cybersecurity expertise with AI engineering knowledge.

CMMC RP-1372. 24+ years in cybersecurity and AI. Free consultation.

NIST AI
RMF Aligned
EU AI Act
Ready
24+
Years Cybersecurity
100%
Assessment Coverage

Key Takeaways

  • 85% of AI projects fail to assess risks before deployment (Gartner 2024), creating liability, compliance, and safety exposures that surface only after launch.
  • The EU AI Act imposes fines up to 35M EUR or 7% of global revenue for non-compliant high-risk AI systems. Risk assessment is now a legal requirement, not a best practice.
  • NIST AI RMF provides the authoritative framework for AI risk management in the U.S. Petronella maps every assessment to its four core functions: Govern, Map, Measure, Manage.
  • AI risk assessment protects against technical, legal, and reputational harm by identifying bias, security vulnerabilities, privacy violations, and safety failures before deployment.
Our Services

What We Deliver

Risk Identification and Categorization

We identify all potential AI risks across technical (model failure, adversarial attacks), legal (bias, privacy), operational (availability, accuracy), and reputational categories, mapped to your specific use case and industry.

Bias and Fairness Assessment

Systematic testing for demographic bias, disparate impact, and fairness across protected characteristics. We evaluate training data, model outputs, and decision patterns using quantitative fairness metrics.

Security Risk Analysis

Assessment of AI-specific security risks: prompt injection, data poisoning, model extraction, adversarial examples, and supply chain vulnerabilities. Mapped to OWASP LLM Top 10.

Privacy Impact Assessment

Evaluation of data handling practices, consent requirements, data minimization, and cross-border transfer risks. Mapped to HIPAA, CCPA, GDPR, and state privacy laws.

Regulatory Compliance Mapping

Gap analysis against NIST AI RMF, EU AI Act, HIPAA AI provisions, state AI laws, and industry-specific AI regulations. We identify which requirements apply and what documentation is needed.

Risk Mitigation Roadmap

Prioritized remediation plan with specific technical, procedural, and governance recommendations. Each risk includes probability, impact, and recommended controls with implementation guidance.

Comparison

AI Risk Assessment Approaches Compared

ApproachCheckbox CompliancePetronella AI Risk Assessment
Framework alignmentSingle frameworkNIST AI RMF + EU AI Act + industry
Technical testingMinimalFull security + bias + safety testing
Risk quantificationQualitative onlyQuantitative with financial impact
RemediationGeneric recommendationsSpecific, implementable actions
Ongoing monitoringAnnual reviewContinuous risk monitoring
Regulatory readinessPartialComplete documentation package
Expert-Led

Led by Craig Petronella

Craig Petronella founded Petronella Technology Group in 2002 with 30+ years of cybersecurity and AI expertise. A CMMC Registered Practitioner (RP-1372), Craig combines security-first thinking with deep AI engineering to deliver solutions that are both powerful and secure.

FAQ

Frequently Asked Questions

When should we perform an AI risk assessment?
Before deploying any AI system, before expanding AI use to new data types or user groups, after significant model updates, and annually for existing AI deployments. Proactive assessment is far less expensive than post-incident remediation.
Which regulations require AI risk assessment?
The EU AI Act requires risk assessment for high-risk AI systems. NIST AI RMF recommends it for all AI deployments. HIPAA requires risk analysis for AI processing PHI. Many state AI laws (Colorado, Illinois, others) mandate bias assessments for automated decision systems.
What is the NIST AI Risk Management Framework?
NIST AI 100-1 is the U.S. government framework for managing AI risks. It has four core functions: Govern (establish oversight), Map (understand context and risks), Measure (analyze and assess risks), and Manage (prioritize and act on risks). Petronella aligns all assessments to this framework.
How long does an AI risk assessment take?
A single AI system assessment takes 2-4 weeks. Enterprise-wide AI portfolio assessments take 4-8 weeks. Complexity factors include the number of AI systems, data sensitivity, regulatory requirements, and whether custom testing is needed.
Do you help implement the risk mitigation recommendations?
Yes. Unlike pure consulting firms, Petronella has full technical capability to implement recommended controls: security hardening, bias mitigation, monitoring deployment, policy development, and compliance documentation.

Assess AI Risks Before They Become Problems

Schedule a free AI risk consultation. We will evaluate your AI portfolio, identify the highest-priority risks, and recommend a structured assessment approach.

Petronella Technology Group, Inc.

5540 Centerview Dr. Suite 200, Raleigh, NC 27606

Phone: 919-348-4912

petronellatech.com