Groundbreaking. Transformative. Disruptive. Powerful. All of these words can be used to describe generative artificial intelligence (gen AI). However, other descriptors also include puzzling, unclear, ambiguous, and risky.

For businesses, gen AI represents the massive potential to enhance communication, collaboration, and workflows across their organizations. However, along with AI advancements come new and enhanced risks to your business. Risks to data security, cybersecurity, privacy, intellectual property, regulatory compliance, legal obligations, and brand relationships have already emerged as top concerns among business leaders and knowledge workers alike.

To get the most benefit from AI-powered technology, business leaders need to manage and mitigate the wide array of security risks it poses to their employees, customers, brand, and business as a whole. Successfully balancing the risks with the rewards of gen AI will help you manage security at the pace of innovation.

In this article, we’ll demystify the key security risks of AI for businesses, provide mitigation strategies, and help you confidently deploy secure generative AI solutions.

The Importance of AI Security

Before we get into the key generative AI security risks, let’s first discuss what’s at stake for businesses if they don’t do their due diligence to mitigate such risks. Generative AI security risks can affect four primary stakeholder groups: your employees, your customers, your brand, and your business.

• Employees: The first group that you must protect with your generative AI security strategy is your workforce. Unsecured AI use and improper AI training could expose sensitive personal and professional information, put your team at risk of using biased outputs, and, ultimately, lead to employees losing trust in your company.
• Customers: Another key group are your customers. Inadequate AI cybersecurity could lead to the mishandling of customer data, breaches of privacy, and loss of customer trust and business. AI security lapses that impact your operations could also lead to a poor customer experience and a dissatisfied customer base.
• Brand reputation: While employee and customer confidence significantly impact your brand image, negative publicity as a result of an AI security breach, noncompliance, or other AI-related legal issue could also damage your brand reputation. 
• Business operations: Last but certainly not least, your entire business is at stake when it comes to AI security. AI cybersecurity incidents can lead to substantial financial losses from data recovery costs, legal fees, and potential compensation claims. Also, cyberattacks targeting your AI systems can disrupt business operations, impacting your workforce’s productivity and your business’s profitability.

Not only can AI security breaches impact your ability to hire and retain talent, satisfy and win customers, and maintain your brand reputation, they could also disrupt your security operations and business continuity as a whole. That is why it is essential to understand the gen AI security risks and take proactive steps to mitigate them.

Now, let’s unpack key gen AI security risks that leaders and workers alike must be aware of so you can safeguard your business.

Key Generative AI Security Risks

Whether it’s data breaches and privacy concerns, job losses, ethical dilemmas, supply chain attacks, or bad actors, artificial intelligence (AI) risks can cover many areas. For the purpose of this article, we are going to focus squarely on generative AI risks to businesses, their customers, and their employees.

We categorize these generative AI security risks into five broad areas that organizations need to understand and include in their risk-mitigation strategies:

• Data risks: Data leaks, unauthorized access, insecure data storage solutions, and improper data retention policies can lead to security incidents such as breaches and unintentional sharing of sensitive data through gen AI outputs. 
• Compliance risks: Failure to comply with data protection laws such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA) can result in significant legal penalties and fines. Additionally, missing or lacking documentation can put you at risk of failing compliance audits, further affecting your company’s reputation.
• User risks: Improper gen AI training, rogue or covert AI use, or inadequate role-based access control (RBAC) might lead to employees compromising your organization. Employees using the technology could unintentionally create misinformation from biased or inaccurate AI outputs or allow for unauthorized access to your data and systems.
• Input risks: Manipulated or deceptive model-training data and even unsophisticated user prompts into your gen AI tool could impact its output quality and reliability.
• Output risks: Bias, hallucinations, and other breaches of responsible AI standards in the large language model development can lead to discriminatory, unfair, and harmful outputs. 

Understanding these key generative AI security risks is the first step in protecting your business from potential cyberthreats. Next, let’s explore practical steps and best practices that you can follow to mitigate these generative AI risks, ensuring a secure and successful deployment of AI technologies.

Generative AI Security Best Practices

To create an effective risk-management strategy, consider implementing the following security best practices and initiatives:

How to mitigate data risks:

• Ensure that your generative AI vendor complies with all relevant data protection and storage regulations and incorporates robust data anonymization and encryption techniques.
• Use advanced access control mechanisms, such as multi-factor authentication and RBAC.
• Regularly audit AI systems for data leakage vulnerabilities.
• Employ data masking, data sanitization, and pseudonymization techniques to protect sensitive information.
• Establish and enforce clear data retention policies to ensure data is not retained longer than necessary.

How to mitigate compliance risks:

• Ensure your AI systems comply with relevant data protection regulations (e.g., GDPR, CCPA, HIPAA) by keeping up-to-date with legal requirements.
• Regularly audit your AI systems and AI providers to ensure ongoing compliance with data protection regulations.
• Maintain detailed documentation of AI cybersecurity practices, policies, and incident responses.
• Use tools to automate compliance monitoring and generate audit reports.

How to mitigate user risks:

• Invest in secure, enterprise-grade gen AI solutions that your entire workforce can use and provide robust acceptable use policies of the technology.
• Implement strict user access policies to ensure that employees have access only to the data necessary for their roles and transparently monitor user activities for suspicious behavior.
• Invest in the AI literacy of your entire workforce so employees across levels, roles, and generations can use AI apps and tools safely and effectively.
• Conduct regular security awareness training for employees to recognize and report potential threats.

How to mitigate input risks:

• Implement adversarial training techniques, such as red teams, to spot vulnerabilities and make gen AI models robust against malicious inputs.
• Use input validation and anomaly detection to identify and reject suspicious inputs.
• Establish secure and verified data collection processes to ensure the integrity of your and your vendor’s training data.
• Regularly review and clean training datasets to remove potential data corruption attempts.

How to mitigate output risks:

• Implement robust, human-in-the-loop review processes to verify the accuracy of AI-generated content before dissemination.
• Invest only in gen AI partners that have transparent and explainable AI models and machine learning algorithms so you can understand and validate their AI decision-making processes.
• Conduct bias audits on AI models to identify and mitigate any biases present in the training data.
• Diversify training datasets to ensure representation and reduce bias.

By implementing these practical steps and best practices, you can effectively mitigate the security risks associated with gen AI. Protecting your data, ensuring compliance, managing user access, securing inputs, and validating outputs are critical to maintaining a secure AI environment.

Evaluating Vendors’ AI Security Claims

Once you’re aware of the key generative AI risks and know how to mitigate them, it’s time to evaluate potential gen AI vendors. Your security team will need to ensure they meet your company’s standards, align with your security posture, and support your business goals before investing in their AI technology. 

Vendors often make various security claims to attract potential buyers. To effectively evaluate these claims, take the following steps:

1. Request detailed documentation: Ask for comprehensive documentation detailing the vendor’s security protocols, certifications, and compliance measures.
2. Conduct a security assessment: Perform an independent security assessment or engage a third-party expert to evaluate the vendor’s security practices and infrastructure.
3. Seek customer references: Request that the vendor provide current or past customer references that speak to their experiences with the vendor’s security measures.
4. Evaluate transparency and responsible AI: Ensure that the vendor can provide transparent documentation about their security and responsible AI practices, can explain their AI model, and is responsive to any security-related inquiries or concerns.

Manage Generative AI Security Risks With Grammarly

At Grammarly, we’re both a builder and buyer of AI technology with over 15 years of experience. This means we understand the complex security risks that businesses face when implementing gen AI tools across their enterprise. 

To help businesses take proactive measures to address the key AI security risks, protect their customers and employees, and uphold their high brand standards, we’re happy to share the frameworks, policies, and best practices that we use in our own enterprise. 

• The Gen AI Decision Maker’s Guide for IT Buyers
• Grammarly’s Framework for Safe Generative AI Adoption
• Grammarly’s Responsible AI Principles
• Your Roadmap to Enterprise-Wide Gen AI Adoption

Remember, taking measured steps to mitigate generative AI security risks doesn’t only protect your business—it protects your employees, customers, and brand reputation, too. Stay informed, stay vigilant, and stay secure.