AI Risk Management in 2026: AI Moves into Production

AI is everywhere. From ChatGPT drafting emails to GitHub Copilot writing code, 77% of organizations now engage with AI in some form, with more than a third already deploying it in production systems.

But as adoption grows, many organizations underestimate the security risks that come with it.

For example, a significant portion of this adoption is driven by shadow AI — employees using unapproved AI tools without IT oversight. Shadow tools bypass traditional security controls and creates immediate visibility gaps for the organization.

In this article, we’ll take a practical look at AI risk management. We’ll cover the technical and non-technical risks, explain how to apply the NIST AI Risk Management Framework, and outline the key challenges organizations face when implementing it.

What is AI risk management?

The growing use of AI has introduced new categories of risk. AI risk management is a specialized branch of risk management focused on identifying, evaluating, and mitigating the risks associated with deploying and using artificial intelligence, while ensuring AI systems are used responsibly.

AI risk management does not replace existing security, privacy, or enterprise risk practices. Instead, it builds on them, often through structured risk frameworks designed to guide how AI-specific risks are identified and addressed. These include:

• NIST AI Risk Management Framework: Structured guidance developed in the U.S.
• ISO/IEC 42001: International standard for AI management systems.
• EU AI Act: Regulatory requirements for AI systems used in the EU.
• IEEE 7000 series: Technical standards focused on ethical AI design.

In this article, we'll focus on the NIST framework, which offers practical, actionable guidance for organizations at any stage of AI adoption.

(Related reading: AI data management.)

Risks associated with AI

AI systems introduce both technical and non-technical risks. Effective risk management requires understanding both.

Technical risks

Data privacy risks. AI models trained on large datasets can inadvertently retain sensitive information, including personally identifiable information (PII). When models generate responses or are probed through carefully crafted prompts, they can leak this data, breaching regulations like GDPR and triggering penalties.

This risk is often exploited through prompt injection attacks, where malicious actors use specifically crafted inputs to bypass a model's safety filters and trick it into revealing restricted data or internal system instructions.

In one widely cited case, employees at Samsung accidentally leaked confidential source code and internal documents with ChatGPT. Samsung subsequently banned ChatGPT from company devices.

Bias in AI models. When training data reflects historical bias, models learn and perpetuate it. For example, if hiring managers historically favored certain demographics, an AI trained on those decisions will likely continue the same pattern unless corrective steps are taken.

In May 2025, a federal judge certified a class-action lawsuit against Workday after allegations that its AI-powered screening tools disproportionately rejected applicants over 40, resulting in hundreds of automated rejections without interviews.

(The concept of AI TRiSM ties together trust, risk, and security management.)

Inaccurate results. AI models trained on outdated or incomplete data produce unreliable outputs. A model trained on consumer behavior during the 2020 COVID-19 pandemic, for example, cannot reliably predict post-pandemic conditions years later, leading to flawed forecasts and poor decisions.

In 2024, a Canadian tribunal ruled against Air Canada after its customer-service chatbot incorrectly told a passenger they could claim a bereavement fare refund retroactively. When the airline refused to honor that guidance, the tribunal found Air Canada responsible for the chatbot’s error and ordered it to compensate the customer.

Overfitting. This occurs when a model becomes too specialized in its training data. Overfitting is dangerous because it creates a false sense of security based on historical data while failing to generalize to new, emerging threats.

A fraud detection system may perform well on historical, known fraud cases, for example, but fail to identify new tactics, leaving organizations exposed despite strong performance in other areas.

Non-technical risks

Ethical and social risks. Beyond technical failures, AI raises broader questions about its impact on people. As organizations automate tasks – from customer service to data analysis – they must consider workforce displacement and their responsibilities to employees whose roles may be affected.

(Read more about AI ethics.)

Reputational damage. AI systems introduce reputation risks in multiple ways. Discriminatory outcomes, such as biased hiring decisions, can trigger public backlash. Even when systems function as intended, some customers may react negatively to impersonal or automated interactions, directly affecting trust and revenue.

In 2024, McDonald’s shut down its AI-powered drive-thru ordering tests after viral videos showed customers repeatedly struggling with the system, leading to public mockery and widespread complaints. The company ended the pilot across more than 100 U.S. locations, underscoring how visible AI deployments can damage brand perception even without a data breach or security incident.

Regulatory risks. AI regulation is evolving quickly. New frameworks and stricter interpretations of existing laws mean organizations must comply with an expanding set of requirements. Failure to adapt can result in fines, legal action, or restrictions on AI use.

For example, in 2024, the U.S. Federal Trade Commission launched its first coordinated AI enforcement sweep, penalizing companies for misleading AI claims – most notably fining DoNotPay for marketing its product as an “AI lawyer” despite lacking the capabilities it advertised – and reinforcing that existing consumer protection laws apply fully to AI systems.

NIST AI Risk Management Framework

Managing AI risk requires a structured approach. Without a framework, organizations often miss critical risks or lack a clear plan for responding when issues arise.

The NIST AI Risk Management Framework (AI RMF) provides that structure. Developed by the U.S. National Institute of Standards and Technology, NIST has been adopted by federal agencies, Fortune 500 companies, and startups across the world. The framework is widely used, freely available, and practical for organizations of different sizes and levels of AI maturity.

Some organizations may also need to meet additional legal requirements. For example, EU companies must comply with the EU AI Act.

Even so, the NIST framework provides a solid foundation for managing AI risk, built around four core functions we’ll look at below. That structure, however, does not determine how much risk an organization should accept or who is accountable when trade-offs arise — those decisions remain the organization’s responsibility.

(Learn about common risk management frameworks.)

Govern

Define the AI system's scope and establish who's accountable for managing its risks.

Start by clarifying what the system is intended to do: where it will be deployed, who will use it, and who may be affected by it. Without this clarity, it’s difficult to identify risks that are relevant to the specific use case.

Next, assign responsibility. Who owns risk assessments? Who approves deployments? How are risks escalated? These roles should be documented and reviewed regularly as the system evolves.

Map

Identify and categorize the risks associated with the AI system.

This involves analyzing where risks such as data exposure, bias, or unreliable outputs may occur. It often requires testing the system, consulting stakeholders with different perspectives, and learning from issues encountered during past AI deployments.

The goal is to create a comprehensive “map” of potential risks, organized by type, severity, and point of origin.

Measure

Evaluate each risk based on its likelihood and potential impact.

Measurement can be quantitative (risk scoring, statistical analysis), qualitative (expert judgment, scenario analysis), or a combination of both. Importantly, assessment should continue after deployment. Real users interact with systems in unexpected ways, edge cases emerge, and model performance can drift as data patterns change — the common model drift problem.

Risk scoring helps you prioritize the issues that matter most. You can’t eliminate all risk, so direct resources toward high-impact, high-probability risks.

Manage

Begin taking action to address priority risks. Examples include:

• Implementing access controls and encryption to protect data.
• Establishing incident response plans to handle breaches.
• Deploying continuous monitoring to detect issues early.

Beyond prevention, organizations need clear response processes. When incidents occur, teams must be able to contain the issue, communicate with affected parties, and restore normal operations quickly.

Challenges in AI Risk Management

Even with frameworks like NIST, organizations still face obstacles when implementing AI risk management.

Lack of standardized metrics

Some AI risks are genuinely hard to quantify. Others can be measured but lack shared thresholds for what counts as acceptable. Even when organizations assign scores or ratings, those numbers don’t always translate into clear decisions.

In hiring contexts, regulators have historically relied on benchmarks such as the “four-fifths rule,” a guideline developed decades before modern AI to flag potential discriminatory outcomes. In 2023, the U.S. Equal Employment Opportunity Commission clarified that this standard also applies to automated and AI-driven hiring tools. Even so, meeting such thresholds does not, in itself, resolve questions of fairness, leaving organizations to make judgment calls about impact and risk tolerance.

Without common standards, risk assessments often produce numbers without clear meaning or guidance.

Third-party opacity

Many organizations rely on commercial AI models like GPT-4, Claude, or Gemini, but can't access each model’s training data, internal architecture, or security measures. This creates blind spots: when something goes wrong, you're responsible for fixing it, but you can't see inside the system to diagnose the problem.

(Learn about observability for LLM systems.)

Speed vs. thoroughness

AI can deliver value quickly, which creates pressure to deploy fast. But proper risk assessment, such as testing edge cases or stress-testing controls, takes time. Skipping these steps invites avoidable risk and often leads to problems that are far more costly to fix later.

Evolving threat landscape

New attack methods targeting AI systems continue to emerge. While the core risk management process remains valid, organizations must continually identify new threats and adjust controls over time, rather than treating risks as static or risk management as a one-off exercise.

Managing AI risk is a continuous process

AI adoption shows no signs of slowing, and so the stakes of managing AI risks continue to grow.

The risks are real: data breaches, algorithmic bias, regulatory penalties, and reputational damage all threaten organizations.

Frameworks like NIST provide a structured, repeatable process for identifying threats and responding when issues arise. With a framework like this in place, organizations can deploy AI with confidence rather than crossing their fingers and hoping for the best.

FAQs about Risk Management for AI