DefinitionRiskFramework

What is AI risk management?

AI risk management is the systematic process of identifying, assessing, and mitigating risks associated with your organization's use of AI systems. It covers data security, compliance, operational, reputational, and ethical risks from AI adoption.

Glossary

AI terms explained

50+ terms

Defined

Risk Categories

Key AI risk categories

Every feature designed to help your team work smarter with AI.

Data security risks

Risks from sensitive data exposure to AI models, data breaches, and unauthorized access to AI systems and their outputs.

Compliance risks

Risks from violating data protection, privacy, and AI-specific regulations like GDPR, HIPAA, and the EU AI Act.

Operational risks

Risks from AI system failures, hallucinations, and over-reliance on AI for critical business decisions.

People risks

Risks from inadequate training, shadow AI usage, and resistance to AI governance policies.

Reputational risks

Risks from AI-generated content that is inaccurate, biased, or inappropriate, damaging your organization's reputation.

Strategic risks

Risks from poor AI investment decisions, vendor lock-in, and failure to adapt to rapidly changing AI capabilities.

Benefits

Why AI risk management is essential

Identify and address AI risks before they become incidents

Meet regulatory expectations for documented AI risk assessment

Enable confident AI adoption with understood and managed risks

Protect your organization's reputation and customer trust

Allocate security and governance resources where they matter most

Build organizational resilience against rapidly evolving AI threats

FAQ

Frequently asked questions

What framework should we use for AI risk management?

The NIST AI Risk Management Framework is the most widely adopted. ISO 42001 provides an international standard. TeamPrompt helps with the data protection and governance aspects of any framework you choose.

How does TeamPrompt reduce AI risk?

TeamPrompt reduces data security risk through DLP scanning, compliance risk through audit trails and access controls, and operational risk through standardized prompts and governance policies.

How often should we reassess AI risks?

Conduct a formal review quarterly and after any significant change in AI tools, usage patterns, or regulations. Continuous monitoring through analytics and DLP detections supplements periodic reviews.

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How it works

Three steps from install to full AI security coverage.

Install

Add the browser extension to Chrome, Edge, or Firefox — or deploy it to your whole team via MDM. No proxy or VPN needed.

Configure

Enable the compliance packs for your industry, set DLP rules, and add your team's prompts to the shared library.

Protected

Every AI interaction is scanned in real time. Sensitive data is blocked before it leaves the browser. Your team has a full audit trail.

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