Generative AI (GenAI) stands at the zenith of technological breakthroughs, far surpassing the awe brought about by the introduction of the Internet. The world began to look at innovation through a unique lens when the minds behind OpenAI revealed ChatGPT to the masses.
Research conducted by McKinsey Global Institute in 2023 cited GenAI's potential impact on annual revenues across industries globally at $2.6 to $4.4 trillion. The report further listed a myriad of use cases where the technology could significantly enhance productivity, such as auto-generating software codes and providing interactive customer support.
However, great powers often have the natural tendency to corrupt themselves when left unchecked. Hence, AI has the potential to create biased user profiles, contribute to hate speech, produce discriminatory remarks, or breach users' data privacy.
Here, AI compliance comes into the picture. It is the way industries can deploy and use AI systems securely and responsibly.
This blog discusses the definition and significance of AI compliance, the challenges enterprises face when implementing it, the consequences of non-compliance, and the best practices to enable the safe and compliant use of AI.
What is AI Compliance?
AI compliance refers to the adherence to regulations, industry standards, and frameworks built to govern the deployment, development, and use of Artificial Intelligence systems and the associated data. As mentioned earlier, AI can have either a positive impact on society, the economy, or the environment or a detrimental impact in the form of bias, discrimination, hate speech, fake journalism, privacy breaches, etc.
Hence, AI laws and frameworks ensure that they cover all the critical aspects of the AI development lifecycle to ensure its safe and responsible use. For example, AI laws ensure that individuals’ data privacy rights are upheld; thus, these regulations require that the data used to train AI or large language models (LLMs) is collected and used legally and ethically. Take, for instance, the Lensa AI controversy, where digital artists claimed that the application stole and used their art to train the application without their consent.
Similarly, AI compliance requires organizations to ensure that their AI systems aren’t used to discriminate against any particular group or deceive people. AI ethical and governance frameworks also ensure that AI systems are deployed and used responsibly for the benefit of society as a whole.
These laws further aim to protect AI infrastructure itself from emerging threats, such as AI poisoning, exfiltration, etc., that could expose enterprises to threats like data breaches. To put things into perspective, 60% of businesses cite they are still unprepared for AI-augmented cyber attacks.
The Importance of Compliance with AI Regulations
Regardless of the complexity of AI laws or ethical standards, compliance with emerging AI regulations is critically important. Let’s take a quick look at some of the reasons why businesses must ensure compliance:
Ethical AI Deployment
In 2018, an e-commerce giant had to remove its experimental AI-powered hiring tool due to discriminatory results against women applicants. Bias and discrimination are among the most common yet serious risks inherent in some AI systems. These risks enter the AI development lifecycle either through the creator or due to the use of biased datasets. Apart from bias, inequality, dependence on AI, unfairness, and rogue AI are some of the other risks that may result in significant political, societal, and environmental harm.
From an enterprise perspective, when bias in AI systems goes untreated, it may produce applications with distorted results that could lead to reputational harm, regulatory penalties, and loss of customer trust.
Learn More About Ethical AI
Proactive AI Risk Management & Mitigation
The unification of multi-cloud adoption and LLMs has enabled enterprises to ensure swift scalability of their AI landscape. However, this unification has further led to a myriad of other privacy, security, and governance risks. For instance, poor data retention and minimization policies or an ineffective consent management system may invite legal risks. Lacking robust safeguards around these risks may welcome unwanted regulatory fines and penalties.
Similarly, there are certain risks associated with AI security, such as a lack of proper AI models’ entitlements or access controls on data flows. Without proper measures to identify and mitigate those risks could lead to cybersecurity incidents.
AI Transparency & Accountability
AI compliance significantly impacts transparency in AI models or systems and accountability. These critical components allow organizations to sporadically audit, assess, and explain the decisions made by their AI models.
GenAI models require a high volume of data to be trained. It is important to ensure the quality of the data. Often, AI systems are trained using corrupt data, which leads to inappropriate or hallucinatory responses. Similarly, some AI bots have been seen to be corrupted through toxic prompts, resulting in hate speech. Here, AI regulatory compliance plays a significant role by necessitating that the AI systems offer a certain level of explainability, which could help enterprises trace the source of the problem and suggest corrective measures.
Current AI Regulatory Landscape
AI is the door to transformative innovations. However, this revolutionary technology is fraught with security and privacy risks. Hence, as data privacy and protection laws now serve as critical obligations for businesses, AI compliance is now equally considered a permanent foundation of the current global legal frameworks.
The EU Artificial Intelligence (AI) Act
Currently, the European Union Artificial Intelligence Act (EU AI Act) leads the global race of AI governance, followed by the US AI Executive Order and Canada’s Artificial Intelligence and Data Act (AIDA), to name a few.
The EU AI Act will enter into force on August 01, 2024, and become applicable in a graduated approach. Taking a risk and purpose-based approach, the law imposes a number of obligations on entities dealing with AI systems in different capacities. The law also demands setting up the European Artificial Intelligence Board (EAIB) as a separate regulatory body responsible for, among others, coordination among national competent authorities and providing advice on the implementation of the EU AI Act.
The EU AI Act categorizes AI systems into separate groups based on the level of risks associated with each category. These categories include AI systems with Unacceptable Risks, High Risks, Limited Risks, and Minimal Risks. The severity of the obligation directly corresponds with the risk associated with a particular AI system. For instance, AI systems that fall under the Unacceptable Risks category are prohibited from being deployed or used as they pose serious risks to people’s safety, livelihood, and fundamental rights.
The EU AI Act is designed such that it also honors the fundamental rights of users afforded under the EU General Data Protection Regulation (GDPR), concerning personal data processing.
NIST’s Artificial Intelligence Risk Management Framework
The National Institute of Standards and Technology (NIST) rolled out its first AI framework in early 2023. The purpose of the NIST AI RMF 1.0 is to give organizations a strategic roadmap that could enable trustworthiness in AI systems’ development, deployment, and usage and help foster responsible AI use.
The NIST AI framework provides enterprises with a strategic way to identify, evaluate, and mitigate AI risks. This will enable enterprises to deploy AI systems ethically while seamlessly navigating potential harm. The framework further fosters accountability and transparency when developing and using AI models and applications.
The framework is divided into two sections. The first section discusses and defines the core aspects of a trustworthy AI system. The second section outlines the best practices for implementing the framework while reducing risks throughout the AI lifecycle development. The second part is further segregated into four distinct functions: Govern, Map, Measure, and Manage.
Learn More About US Federal AI Laws
The Critical Challenge & Risk Hindering AI Compliance
A survey by Accenture hints at enterprises' favorable attitude toward AI compliance, citing that a whopping 77% consider future AI regulations a company-wide priority. However, a myriad of challenges and risks often impede an enterprise’s effort to meet compliance. Take, for instance, the lack of visibility of shadow AI.
Shadow AI refers to AI models, systems, or applications that are deployed and used without robust security controls and policies. One may refer to such systems as unsanctioned models, as IT teams rarely have any knowledge of these models nor have sanctioned their use.
The proliferation of shadow AI across an enterprise environment exposes it to a series of vulnerabilities and risks. For instance, as Organizations do not have visibility into these systems, they do not know the risks associated with them. Not knowing the risks leads to issues like AI poisoning, toxicity, AI hallucination, etc. Similarly, large language models (LLMs) leverage volumes of training data to yield smart responses. Not having proper security controls across these models and the training data may lead to malicious attacks, data manipulation, or data leakage.
Ultimately, shadow AI may expose organizations to not only compliance violations but also security risks and reputational loss.
Shadow AI is one of the many challenges that hinder an enterprise’s compliance effort. For instance, explainability plays a crucial role in fostering transparency and accountability in AI development and usage. However, due to the lack of proper controls and policies around AI models and training data, it gets challenging to ensure AI explainability and accountability.
Best Practices to Simplify & Meet AI Compliance
To take strategic control of your AI landscape, Securiti recommends considering the following five-step best practices for AI governance and compliance.
Discover & Catalog AI Models
It is critically important for enterprises to discover AI models across their public, private, and SaaS environments to get a complete picture of their AI landscape. This should include sanctioned and unsanctioned AI model discovery across production and non-production environments. Metadata associated with the AI model's properties and characteristics should also need to be cataloged to evaluate its impact, risks, and compliance needs. The discovery and cataloging process needs to be automated and timely updated so it may reflect the changes as new AI models are added to the environment.
Assess Risks & Classify AI Models
Evaluate the aspects that influence the AI models across the public, private, and SaaS environments. This includes the assessment of both public clouds and SaaS vendors. Document what AI models the vendors are feeding, how they are using your data, and how their overall services influence your AI landscape. After the assessment, provide risk ratings based on the EU AI Act and other regulations or standards to cover key aspects, such as AI toxicity, hallucination, etc. These ratings would enable you to determine which AI models to sanction or block.
Map & Monitor Data+AI Flows
Apart from discovering AI models and evaluating risks, it is also critical to understand how these models interact with other data systems, datasets, processes, and policies. This enables organizations to identify dependencies and potential risks and remediate them proactively.
Implement Data+AI Controls for Privacy, Security and Compliance
Establish proper controls and policies around sensitive data, making sure that it doesn’t make its way to the AI models without robust safeguards. This works in two ways. First, there needs to be controls on the data input side, ensuring that data going through the AI models is properly inspected, classified, and sanitized. For instance, sensitive data should be tokenized, masked, or anonymized before it flows to AI systems. Secondly, there needs to be controls around data flowing out of the AI models. LLM firewalls need to be implemented to protect the data against risks of prompt injections or AI exfiltration, etc.
Comply with Regulations & Industry Standards
Conduct assessments to evaluate the current status of compliance with AI regulations and standards. Also, implement a robust framework that automates compliance with global AI laws and industry standards using common grammar, controls, and tests.
Meet AI Compliance Swiftly with Securiti
AI Security and Governance, an integration of Securiti Data Command Center, is built to help organizations develop, deploy, and use AI while leveraging contextual data+AI intelligence and automated controls.
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