Governance Risk and Compliance
Governance, risk, and compliance is often shortened to GRC. It means setting clear rules, checking for harm, and following important laws and standards.
Common uses (where it shows up)
GRC matters anywhere AI affects people, money, safety, or private data.
- Checking models before launch
- Watching for bias, errors, and drift over time
- Keeping records for audits and approvals
- Reviewing vendor claims before buying a tool
- Using AI in hiring, health, finance, security, and customer service
Some non-chat tools used in this area are IBM watsonx.governance, Credo AI, and Holistic AI.
Dive deeper with BonsAI Chat
Use the embed below to explore examples, controls, and questions to ask before you rely on a system.
What AI is good at (and bad at)
The NIST AI Risk Management Framework and the OECD AI Principles both support a simple idea: AI helps most with repeat work, but people still need to make the hard calls.
- Good at: sorting large sets of data, finding patterns, flagging unusual activity, and helping with repeat checks
- Bad at: understanding context, handling new edge cases, explaining itself clearly, and making fair decisions on its own
Risks you must take seriously
The NIST AI Risk Management Framework says AI risks can affect people, organizations, society, and the environment. The FTC action on AI compliance claims is also a useful warning: a tool may be marketed as safe or compliant even when the proof is weak.
- Bias that hurts some groups more than others
- Privacy and security failures
- Wrong outputs that look confident
- Over-trusting a vendor or a dashboard
- Poor records, which makes audits and reviews hard
How to use AI safely (simple checklist)
A good starting point is the NIST AI Risk Management Framework. If your work touches the EU, the AI Act is another helpful high-level reference.
- Define the job the tool should do
- Check what data goes in and what data comes out
- Test for accuracy, bias, and failure cases before real use
- Keep a human review step for important decisions
- Save clear logs, approvals, and change history
- Review the system again after updates or drift
How rules and regulators think about it (high level)
The AI Act uses a risk-based approach, which means stricter duties for higher-risk uses. The OECD AI Principles focus on trustworthy AI, human rights, and accountability. In the United States, many teams use the NIST AI Risk Management Framework as practical guidance, and the FTC action on AI compliance claims shows that old rules against unfair or misleading claims still matter.
Questions to ask before you trust a tool
These questions match the spirit of the NIST AI Risk Management Framework and the OECD AI Principles.
- What exact problem does this tool solve?
- What data was it trained or tested on?
- How do we know it works for our users and our setting?
- What can go wrong, and who is harmed if it fails?
- Who approves its use, and who can stop it?
- What evidence supports any claim that it is safe, fair, or compliant?