.svg)
There’s a paradox at the heart of AI compliance: GenAI models like LLMs are, by nature, black boxes. How can regulators define acceptable parameters for the use of data, when they can’t see into the box? And how can CISOs guarantee their AI tools are storing, processing, and protecting data appropriately when they don’t know with 100% clarity what these tools are actually doing?
These aren’t easy questions to answer, but they are critical to the success of compliance professionals seeking to rein in the AI technology their organizations are using.
Let’s consider the key issues and risks, and practical next steps for CISOs lost in the wild, wild west of LLM tech and the shifting sands of AI compliance regulations.
This term covers all the steps that an organization takes to ensure that its use of GenAI aligns with relevant rules and regulations.
From a business and operational point of view, that includes a range of interlocking risk and compliance priorities:
The last one is the kicker because as we said earlier, GenAI is not like any other technology. Instead of taking inputs and delivering outputs in a straightforward way, artificial intelligence actually predicts, manipulates data, and “thinks”. What this means is that AI compliance activities need to be dynamic and always-on, combining deep knowledge of the technology with a sure-footed approach to regulatory change management.
To begin with, compliance professionals in this area need to have an up-to-date understanding of the emerging regulatory frameworks addressing risk and compliance.
The General Data Protection Regulation (GDPR) is a landmark privacy law in the European Union. It governs how organizations handle personal data, and its requirements apply to any organization that does business within the EU, even if they aren’t European. When it comes to AI, GDPR imposes strict requirements on transparency, consent, and the right to explanation. This framework aims to ensure that individuals always understand how organizations are using their data for automated decision-making processes.
The EU AI Act is a comprehensive proposal aimed at regulating AI technologies across the European Union. It classifies AI systems based on their risk level, with the most stringent requirements applied to high-risk applications like biometric identification and critical infrastructure. The Act focuses on ensuring safety, transparency, and accountability in AI development and deployment.
The Algorithmic Accountability Act is a proposed U.S. law addressing the ethical and fairness concerns surrounding AI and automated decision-making systems. If it becomes law, it will require companies to assess the impact of their algorithms, especially in areas like discrimination and privacy, and to take corrective measures.
The U.S. Executive Order on AI, issued in 2019, represents a significant step toward establishing a national strategy for artificial intelligence. The order emphasizes the importance of maintaining American leadership in AI while ensuring the technology's development aligns with core values such as privacy, civil liberties, and national security.
As we saw earlier, compliance frameworks for AI have to contend with the fact that they can’t see into the technology they’re regulating. But the challenges don’t end there: building strong compliance programs is tough for a number of other reasons, too:
AI compliance is, in some sense, an either-or. To be fully compliant, an organization needs to maintain the same standards everywhere - or nowhere at all. Teams like risk management and data management are typically more likely to prioritize compliance issues. But others may be less inclined or less able to do so.
This inconsistency can lead to uneven application of AI policies, resulting in fragmented compliance efforts and potential vulnerabilities.
Traditional risk management frameworks often fall short when applied to AI technologies. These conventional frameworks were built for conventional risks, and the risks that LLMs pose are anything but conventional. Relying on these frameworks may end up being a case of trying to fight square pegs in round holes.
Organizations often rely on third-party vendors and associates for AI solutions, data services, or infrastructure. All of these need to adhere to strict compliance standards in order to avoid contamination because the software supply chain is a source of risk.
If third-party associates handle sensitive data or play any role in AI deployment, organizations must vet them to make sure they align with their own compliance processes. Even the strongest compliance program is only as secure as its weakest link - one overlooked vendor vulnerability can be the crack that sinks the ship.
It’s no secret that AI talent is in critically short supply. And as a subset of this category, “responsible AI” is in even shorter supply. Companies face an uphill struggle to recruit Responsible AI (RAI), or upskill their own people in order to meet the need to deploy AI systems that are more transparent, accountable, and compliant.
Compliance professionals entering AI compliance will find some overlap with the compliance activities they’re used to performing. But they will also find a host of new compliance requirements and unique KPIs.
For example, “privacy by design”: with an AI tool, it’s essential to assess how well privacy has been integrated across the system’s lifecycle, from design to deployment. This includes anonymization techniques and user consent management.
Non-compliance with AI regulations can lead to serious consequences for companies and organizations. Here are some recent examples:
Keep up with the latest rules and regulations to ensure your AI stays on the right side of the law. Remember that these are constantly evolving, so what worked yesterday may be obsolete by next week.
Build privacy protections into your AI from the start to safeguard user data.
Keep your data organized and clean to ensure your AI operates accurately and responsibly.
Keep trained humans in the loop to oversee AI decisions and take responsibility when needed. This ensures that automated actions are ethically sound and correctable when mistakes happen.
Don’t skimp on security protocols: implement strong security measures to protect your AI, GenAI, LLM, and MLfrom cyber threats and unauthorized access.
Thoroughly document every step of your AI processes and conduct regular audits. This helps identify potential weaknesses before they become major issues, which can make all the difference in a crisis.
Train your team on AI compliance so everyone understands their role in maintaining ethical AI practices.
Work with partners and agencies, like CISA and NIST, to stay aligned with best practices and industry standards.
Keep a close eye on your AI systems and constantly refine them to maintain compliance.
Regularly consult with legal experts to navigate the complexities of AI law and regulations.
As early LLM security pioneers, Lasso Security is at the forefront of fostering safe and trustworthy GenAI development. Through our partnerships with industry leaders and decision-makers, we are deeply involved in shaping the future of AI compliance, and helping our customers to stay ahead of the curve without the need to expand their own compliance processes.
And because we know that one size doesn’t fit all, we’ve built customizability right into the heart of the Lasso Security platform. That enables our customers to easily configure policies that align with their own security needs. Talk to our team to learn more about how customized, always-on security could streamline your AI compliance efforts.
As artificial intelligence continues to evolve, so does the regulatory landscape surrounding its use. Staying compliant with these ever-changing regulations is not just a matter of avoiding penalties—it's about positioning your organization as a leader in responsible AI deployment. With our expert guidance and insights, you can confidently navigate the complexities of AI regulations.
Don’t wait for regulatory challenges to catch up with you. By implementing Lasso Security today, you’re not just avoiding future penalties—you’re investing in the ultimate tool for long-term success in the AI-driven world. Stay ahead of the curve, protect your innovations, and lead the charge in secure and compliant AI practices.
.png)