AI Risk Assessment: 4 AI Risks, Case Studies & Top Tools

updated on Oct 26, 2025

As AI becomes central to business operations, AI risk assessment and mitigation is now a strategic priority.

Discover four main types of AI risks supported by real-world examples, leading tools, and legal frameworks and policies that help organizations detect, assess, and mitigate AI risks effectively while maintaining compliance and trust.

What is AI risk assessment?

AI risk assessment is the structured process of identifying, evaluating, and mitigating the risks associated with AI. Unlike traditional IT systems, AI models are adaptive, data-driven, and often opaque. These features make them powerful but also risky.

The purpose of conducting risk assessments in AI is to:

Ensure AI tools are not deployed in ways that cause significant harm.
Assess whether input data and training data are lawful, fair, and representative.
Identify security vulnerabilities and adversarial threats.
Evaluate ethical considerations such as bias, explainability, and fairness.
Document mitigation measures and ensure compliance with laws applicable.

AI risk types and real-life examples

AI technologies expose organizations to multiple types of risks. These can be grouped into four categories:

1. Data risks

AI models rely heavily on data. If input data or training data is flawed, biased, or unlawfully sourced, outcomes will be compromised. Risks include privacy violations, unauthorized sharing, or unrepresentative datasets that disadvantage specific demographic groups.

Here are some specific data risks in AI:

Data security: Threat actors can breach or exfiltrate datasets used for AI training and inference, resulting in unauthorized access, data loss, and compromised confidentiality.
Data privacy: AI systems often process sensitive personal or behavioral data; mishandling this information can cause privacy violations and regulatory penalties.
Data integrity: Flawed, biased, or manipulated data can produce inaccurate predictions, false positives, or systemic bias in AI outputs.
Data provenance: Unclear data sources or undocumented lineage can create accountability gaps and complicate audits under data protection laws.
Data availability: Disruptions in access to essential datasets can impair model retraining and lead to performance degradation or downtime.

Real-life example for data risk: Biased data in hiring

Derek Mobley sued HR software firm Workday, claiming its AI hiring tool automatically rejected him from over 100 jobs due to discrimination based on his race (Black), age (over 40), and disabilities. This case exposes critical data risk and ethical/legal risk in AI-driven hiring.

This bias was projected through two main mechanisms:

The AI was likely trained on biased historical hiring data, which perpetuates past systemic discrimination.
The resulting algorithmic bias systematically screens out qualified candidates based on protected characteristics.

The U.S. Equal Employment Opportunity Commission (EEOC) argues that AI providers like Workday must be held legally accountable for this automated discrimination.

The actions and long-term results following the lawsuit were significant:

Legal scrutiny: The case requires the EEOC to enforce anti-discrimination laws against AI providers.
Mandated solutions: Long-term solutions require companies to regularly audit AI for bias, ensure human oversight in hiring, and maintain transparency.¹

2. Model risks

Complex AI models such as deep learning systems often operate as black boxes. This creates risks for adversarial attacks, misinterpretation, or unexplained outputs.

Some of the specific model risks include:

Adversarial attacks: Malicious actors modify input data to deceive AI models into producing incorrect or biased outputs, undermining decision integrity.
Prompt injections: Attackers embed hidden instructions in prompts to make large language models (LLMs) reveal sensitive data, spread misinformation, or override safety guardrails.
Model interpretability: Complex or opaque algorithms reduce transparency and make it difficult to explain or audit AI decisions, eroding stakeholder trust.
Supply chain attacks: Vulnerabilities in third-party code, pre-trained models, or APIs can be exploited, leading to data leaks or model manipulation.
Model theft and tampering: Stolen or altered model weights and parameters can degrade performance and expose proprietary intellectual property.

Real-life example for model risk: AI hallucinations in legal briefs

Lawyers from Ellis George LLP and K&L Gates were sanctioned for submitting legal briefs containing fake, AI-generated case citations. This case highlights critical model risk known as AI hallucination, where generative AI models invent plausible but false information.

This failure was projected through two main mechanisms:

The AI model’s invention of non-existent legal precedents (hallucination).
Lack of personal verification and oversight by the legal professionals using the AI tool.

This action misled the court and wasted judicial resources.

The actions and results following the sanctions were significant:

Long-term solution: Legal professionals must implement strict human oversight and citation-checking protocols and treat AI as a brainstorming aid, not a substitute for research.
Financial penalties: The law firms were ordered to jointly pay $31,100 in the defendant’s legal fees.
Mandated procedural changes: Judges emphasized that lawyers must personally verify all AI-generated content.²

3. Operational risks

AI deployment introduces operational challenges such as system drift, integration errors, and sustainability concerns. Without regular monitoring, AI programs may deviate from intended goals and produce unreliable outputs.

Model drift and decay: Changing data patterns over time reduce model accuracy, requiring retraining and recalibration to sustain performance.
Sustainability challenges: High computational demand and energy use can lead to inefficiency, elevated costs, and environmental concerns.
Integration issues: Combining AI with legacy systems introduces compatibility gaps, data silos, and new cybersecurity vulnerabilities.
Scalability limitations: Without robust infrastructure and monitoring, expanding AI systems can cause instability, latency, or inconsistent outputs.
Lack of accountability: Few organizations maintain formal responsible-AI boards or governance structures, leaving oversight gaps in critical processes.

Real-life example for operational risk: Safety failure in AI-driven systems

U.S. auto safety regulators opened an investigation into 2.4 million Tesla vehicles equipped with the company’s Full Self-Driving (FSD) software following four reported collisions, including a fatal 2023 crash. This case highlights critical operational risk in AI-driven systems since the FSD system appears to fail in specific operational conditions.

This failure was projected through two main mechanisms:

The software’s inability to perform safely during reduced visibility like sun glare or fog.
The system’s failure to adequately account for or respond to specific environmental conditions, leading to serious consequences.

The investigation focuses on these failures, which resulted in a pedestrian death and other injuries. The National Highway Traffic Safety Administration (NHTSA) is scrutinizing Tesla’s engineering controls and software updates.

The actions and long-term results following the investigation were significant:

Long-term product enhancements: Long-term solutions may require the software to enhance its sensor systems
Improve performance in all weather conditions
Provide greater transparency about the system’s limitations to ensure public safety.
Mandated scrutiny and investigation: NHTSA continues to scrutinize Tesla’s engineering controls and any subsequent software updates.³

4. Ethical and legal risks

AI raises ethical considerations including fairness, explainability, and respect for human rights. Systems that reinforce bias or infringe on privacy can cause potential harms that extend beyond technical errors. In extreme cases, AI may create unacceptable risks by violating data protection laws or undermining democratic processes.

Algorithmic bias and discrimination: AI models trained on biased or unbalanced datasets may perpetuate unfair outcomes in areas such as hiring, lending, or law enforcement, resulting in systemic discrimination against protected groups.
Lack of transparency and explainability: Opaque AI decision-making (“black box” models) can make it difficult for individuals to understand how or why decisions were made, violating ethical standards of accountability and due process.
Autonomy and human oversight failures: Over-reliance on AI for decision-making can undermine human judgment and ethical accountability, especially when automated systems act without meaningful human review or appeal mechanisms.
Infringement of privacy and consent: AI surveillance systems, facial recognition, or behavioral analytics tools may collect and process personal data without explicit consent, breaching privacy laws such as GDPR or equivalent local regulations.
Intellectual property and copyright violations: Generative AI models trained on copyrighted content without authorization can raise serious legal disputes over ownership, attribution, and fair use.

Discover specific AI ethics, generative AI ethics, and AI bias issues and real-world examples.

Real-life example for ethical and legal risk: Algorithmic discrimination in housing

The lawsuit against SafeRent Solutions in November 2024 alleged racial and income-based algorithmic discrimination in its tenant screening system. The ethical bias was not intentional but stemmed from a disparate impact on protected classes, specifically Black and Hispanic applicants using housing assistance.

This bias was projected through two main mechanisms:

The model’s failure to account for the financial benefits of housing vouchers
the model’s over-reliance on credit information.

Plaintiffs argued this unfairly penalized protected groups due to historical inequities resulting in lower credit scores, producing the “same effect as if you told it to discriminate intentionally.”

The actions and results following the lawsuit were significant:

Financial settlement: The lawsuit resulted in a settlement against SafeRent exceeding $2.2 million.
Mandated product changes: SafeRent was required to make fundamental changes, including:
- Prohibiting the use of its proprietary score feature in screening reports when applicants utilize housing vouchers
- Requiring third-party validation for any subsequent screening scores.⁴

The table below maps AI risk types to specific AI solutions (Generative AI, Traditional AI, or Agentic AI):

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For more details, check out:

Legal frameworks and policies

Organizations must embed AI risks within robust risk management frameworks to ensure the responsible design, development, and deployment of artificial intelligence. These global and regional initiatives establish the necessary standards for responsible AI across the entire lifecycle.

NIST AI RMF (U.S.): Provides a structured approach to managing AI risks using four core functions: Govern, Map, Measure, and Manage. It guides organizations to incorporate trustworthiness (fairness, robustness, explainability) into AI systems.
EU AI Act (Europe): Prohibits “unacceptable” AI (e.g., social scoring, real-time biometric ID). Mandates strict controls for “high-risk” AI (health, employment, law enforcement), including required risk assessments, data quality, logging, and human oversight.
ISO/IEC standards: Specifies requirements for organizations to establish an AI Management System (AIMS), covering ethical use, transparency, risk management, and continual improvement of AI.
OECD principles: Promotes trustworthy AI through five core principles: Inclusive Growth, Human-Centred Values and Fairness, Transparency, Robustness/Security/Safety, and Accountability. Serves as a baseline for national policies globally.

While the US federal approach is currently guideline-based (NIST), several states have enacted binding laws focused on specific high-risk areas, primarily concerning algorithmic discrimination:

California SB 1120 (2024): Takes effect Jan 2025, mandating a “Human-in-the-loop” approach for healthcare. It prohibits insurers from using AI as the sole basis for denying, delaying, or modifying coverage, requiring review by a qualified physician.
Colorado AI Act (2024): Effective Feb 2026, it targets high-risk AI in employment & services. It requires developers and deployers to conduct AI impact assessments, ensure fairness through bias audits, and allow consumer opt-outs.
Illinois AI Employment Law (2024): Effective Jan 2026, it amends civil rights law to forbid AI-driven discrimination (e.g., banning zip-code proxies) and mandates employer notice when AI is used for candidate decisions.
New York City Local Law 144 (2023): Requires a third-party audit and public posting of bias audits for any automated hiring or promotion tool used by NYC employers.

AI risk assessment tools

AI risks can be identified and mitigated by leveraging tools like:

Some of these AI risk assessment tools and frameworks include:

Why is AI risk assessment important?

There are three main reasons why organizations invest in AI risk management:

Legal and regulatory requirements

Regulatory action is moving from warnings to significant financial penalties, making formal AI risk management a compliance necessity. For example:

EU AI Act penalties: Though the Act is newly effective, the EU AI Act sets a tiered fine structure:
- Up to €35 Million or 7% of annual worldwide turnover (whichever is higher) for violations of the prohibited AI practices (e.g., social scoring).
- Up to €15 Million or 3% for non-compliance with obligations for high-risk systems (e.g., lacking proper documentation or data quality).⁵
GDPR precedent: High-profile cases under GDPR, which shares regulatory goals with the AI Act, show the scale of potential fines:
- Meta has faced fines up to €251 million for data breaches⁶
- LinkedIn was fined €310 million for hidden behavioral profiling.⁷

Ethical concerns

AI risks such as algorithmic bias, lack of transparency, and misuse of generative AI lead to significant ethical and social impacts, such as:

Worsening societal inequalities: Biased outcomes reinforce systemic discrimination, leading to real-world harm and undermining trust in automated decision-making.
- Bias risk in criminal justice: The algorithm incorrectly labeled Black defendants who did not re-offend as high-risk at a rate of 45%, nearly twice the rate of their white counterparts (23%). This statistical error pattern exacerbates racial disparities in the justice system.
- Bias risk in healthcare: An AI model used to manage patient care assigned less care to Black patients because it used lower historical healthcare spending as a proxy for health needs. This bias effectively reduced the care Black patients received by over 50% compared to white patients with the same level of illness.

Check out for more on such AI bias real-life examples and their sources.

Business resilience

Managing AI risks helps organizations maintain trust with stakeholders, avoid regulatory penalties, and build trustworthy AI systems that can scale without reputational damage.

For example, Europe has translated this imperative into a financial strategy with the EIB’s AI Governance Capital initiative. This initiative is:

Directing billions of Euros toward AI ventures that prioritize fairness, data privacy, and environmental sustainability.
Directly supporting the EU AI Act, demanding transparency and accountability.
Positioning Europe as the global leader for ethical AI, integrating compliance with innovation.
Mitigating risks (like bias) to secure public trust and support the projected €700 billion European AI market by 2030.
Driving a high demand for professionals in AI law, ethics, and compliance.⁸

Conducting AI risk assessment: Step by step

A typical assessment process includes:

Mapping the system: Define the purpose, scope, and affected stakeholders.
Risk classification: Determine whether the system falls into high risk AI systems, low risk, or other categories under applicable law.
Evaluation: Use a risk matrix to assess the likelihood and severity of risks.
Mitigation strategies: Identify technical and organizational measures to reduce risks.
Monitoring: Ensure continuous oversight and update assessments regularly.

This structured approach ensures risks are identified early and mitigated through both technical measures and governance processes.

Mitigation strategies for AI risks

Organizations can apply different mitigation measures to reduce exposure:

Data governance: Ensure that data scientists apply robust checks to remove bias, ensure representativeness, and comply with privacy laws.
Explainability tools: Increase transparency in decision making by providing human oversight and interpretability.
Security teams: Implement safeguards against adversarial attacks and other security vulnerabilities.
Cross functional teams: Involve compliance, technical, and legal experts in oversight processes.
Impact assessments: Evaluate how AI systems affect different stakeholders and ensure compliance with regulatory requirements.

Business benefits of AI risk management

While demanding, AI risk management yields business benefits:

Regulatory compliance reduces exposure to penalties and litigation.
Operational resilience ensures stability in AI deployment.
Transparency strengthens reputation with customers and regulators.
Informed innovation ensures that risk does not block but enables responsible scaling of AI.
Ethical standards improve trust and align with societal expectations.

Challenges in AI risk management

Despite growing awareness, organizations face persistent challenges:

Regulatory fragmentation: Organizations must comply with different stakeholders and overlapping laws in multiple jurisdictions.
Resource intensity: Implementing mitigation strategies and documentation processes requires investment.
Technical demands: Designing trustworthy AI systems requires expertise in adversarial attacks, fairness, and transparency.
Organizational silos: Without strong collaboration, compliance may remain confined to one function rather than a shared responsibility.