AI is rapidly becoming ubiquitous across business systems and IT ecosystems, with adoption and development racing faster than anyone could have expected. Today it seems that everywhere we turn, software engineers are building custom models and integrating AI into their products, as business leaders incorporate AI-powered solutions in their working environments.
Map out AI usage in your wider ecosystem
You can’t manage your team’s AI use unless you know about it, but that alone can be a significant challenge. Shadow IT is already the scourge of cybersecurity teams: Employees sign up for SaaS tools without the knowledge of IT departments, leaving an unknown number of solutions and platforms with access to business data and/or systems.
Now security teams also have to grapple with shadow AI. Many apps, chatbots, and other tools incorporate AI, machine learning (ML), or natural language programming (NLP), without such solutions necessarily being obvious AI solutions. When employees log into these solutions without official approval, they bring AI into your systems without your knowledge.
As Opice Blum’s data privacy expert Henrique Fabretti Moraes explained, “Mapping the tools in use – or those intended for use – is crucial for understanding and fine-tuning acceptable use policies and potential mitigation measures to decrease the risks involved in their utilisation.”
Verify data governance
Data privacy and security are core concerns for all AI regulations, both those already in place and those on the brink of approval.
Your AI use already needs to comply with existing privacy laws like GDPR and CCPR, which require you to know what data your AI can access and what it does with the data, and for you to demonstrate guardrails to protect the data AI uses.
To ensure compliance, you need to put robust data governance rules into place in your organisation, managed by a defined team, and backed up by regular audits. Your policies should include due diligence to evaluate data security and sources of all your tools, including those that use AI, to identify areas of potential bias and privacy risk.
Establish continuous monitoring for your AI systems
Effective monitoring is crucial for managing any area of your business. When it comes to AI, as with other areas of cybersecurity, you need continuous monitoring to ensure that you know what your AI tools are doing, how they are behaving, and what data they are accessing. You also need to audit them regularly to keep on top of AI use in your organisation.
Use risk assessments as your guidelines
It’s vital to know which of your AI tools are high risk, medium risk, and low risk – for compliance with external regulations, for internal business risk management, and for improving software development workflows. High risk use cases will need more safeguards and evaluation before deployment.
Proactively set AI ethics governance
You don’t need to wait for AI regulations to set up ethical AI policies. Allocate responsibility for ethical AI considerations, put together teams, and draw up policies for ethical AI use that include cybersecurity, model validation, transparency, data privacy, and incident reporting.
Don’t let fear of AI regulation hold you back
AI regulations are still evolving and emerging, creating uncertainty for businesses and developers. But don’t let the fluid situation stop you from benefiting from AI. By proactively implementing policies, workflows, and tools that align with the principles of data privacy, transparency, and ethical use, you can prepare for AI regulations and take advantage of AI-powered possibilities.
Conclusion:
AI regulations may be uncertain, but proactively implementing policies, workflows, and tools that align with the principles of data privacy, transparency, and ethical use can help prepare for future regulations. Don’t let fear of regulation hold you back – start mapping your AI usage, verifying data governance, and establishing continuous monitoring to get ahead of the curve.
FAQs:
Q: Why is mapping AI usage in my ecosystem important?
A: Mapping AI usage in your ecosystem is crucial to understanding and managing AI use in your organisation.
Q: What are the key concerns for AI regulations?
A: Data privacy and security are core concerns for all AI regulations.
Q: How can I ensure compliance with AI regulations?
A: To ensure compliance, you need to put robust data governance rules into place, managed by a defined team, and backed up by regular audits.
Q: What is shadow AI and why is it a problem?
A: Shadow AI refers to AI solutions and tools that are used without official approval, creating potential risks for data security and privacy.
Q: Can I use AI to monitor and regulate other AI systems?
A: Yes, techniques like machine learning models that predict other models’ behaviors (meta-models) are employed to monitor AI.
Q: Why is continuous monitoring important for AI systems?
A: Continuous monitoring is crucial to ensure that you know what your AI tools are doing, how they are behaving, and what data they are accessing.
