Generative AI Risks

The risks of AI are no longer hypothetical. Several organizations have already experienced their proprietary information being leaked through AI-related vulnerabilities and misuse, leading some to ban AI use entirely. But simply banning AI isn't the answer—it's not just risky to use AI, it's risky not to. The key is understanding the real threats and knowing how to manage them.

In this article, we'll explore the complex landscape of AI-related risks and the essential strategies organizations need to protect themselves. We'll examine the most significant challenges generative AI presents and provide actionable frameworks for managing these risks—all while preserving AI's powerful competitive advantages.

What are some Generative AI Risks?

Generative AI is transforming how we work, creating powerful new capabilities across industries. Companies are solving complex problems faster, automating tedious tasks, and unlocking new levels of productivity. But with this rapid advancement comes a growing list of challenges. Organizations have lost millions of dollars through leaked source code, compromised customer data, and exposed trade secrets—all stemming from AI tools being used without proper safeguards.

Understanding these risks requires systematically examining them, from security vulnerabilities to ethical concerns. Let's explore the key areas where organizations need to focus their attention when implementing generative AI.

Security

The security implications of generative AI extend far beyond traditional cybersecurity concerns. Every time an organization feeds data into an AI system, it risks exposing proprietary and sensitive information. These systems don't just process data—they remember it and can accidentally share it later in responses to other users.

Recent incidents highlight these risks. In one case, Samsung developers found their internal source code had been leaked after other employees used ChatGPT for code assistance, causing the company to temporarily ban AI tool usage. In another case, an AI-powered coding assistant leaked internal API keys and credentials through its suggestions.

Organizations must also contend with new types of attacks. Hackers can manipulate AI models through prompt injection attacks, bypassing security controls with carefully crafted inputs. Some attempt to steal proprietary models through extraction attacks, while others poison training data to compromise system behavior. Most concerning, attackers are developing methods to reconstruct private information from the model's training data.

The challenge is compounded because traditional security tools weren't designed for these AI-specific vulnerabilities. Organizations need comprehensive security approaches that address how AI systems process, retain, and share information.

Compliance

Regulators are racing to catch up with AI technology, creating a patchwork of requirements that organizations must follow. Data protection regulations like GDPR and CCPA have specific requirements about how personal data can be processed and stored, yet most AI systems weren't built with these regulations in mind.

The compliance hurdles are substantial. Companies need to track how AI systems use personal data and respond to user requests about their information—including requests to delete it. They must also prove their training data was collected legally and maintain records of every AI-generated decision. For global companies, the challenge multiplies as they navigate different rules across countries.

Regulated industries face even tougher requirements. Healthcare providers using AI must follow HIPAA's strict patient privacy rules. Banks and financial firms need to explain how their AI makes decisions and prove these decisions are fair and unbiased.

Ethics

AI systems can perpetuate real-world biases in ways that are hard to spot and harder to fix. The problems show up everywhere: hiring tools that favor certain groups because they were trained on biased historical data, healthcare systems that work better for some ethnic groups than others, and marketing AI that produces tone-deaf content.

These issues raise tough questions about responsibility. When AI helps make a decision, who's accountable for the outcome? Companies need to balance AI's benefits with their ethical obligations, especially when decisions affect people's lives and opportunities.

Accuracy

AI systems can be convincingly wrong. They sometimes make up facts, invent citations, and generate false information with complete confidence. This isn't just annoying—it's creating real problems across industries.

The examples are sobering. A federal judge recently fined lawyers $5,000 for using ChatGPT in legal research, which had fabricated case citations and fake judicial opinions for their filing. In early 2024, Google had to pause its Gemini AI image generator after it produced historically inaccurate images that went viral.

These accuracy problems affect everything AI touches. Reports and documentation might contain fabricated data. Code can look perfect but hide serious bugs. Data analysis might tell a compelling but false story. Customer service chatbots can confidently give wrong information.

Organizations are finding that checking AI's work is crucial. This means having humans verify important decisions, using tools to fact-check outputs, and regularly testing AI systems for accuracy.

Misuse

The biggest AI risks often come from inside organizations. Employees are using these powerful tools without understanding the risks, and it's leading to serious problems.

Staff paste confidential data into public AI tools without thinking about security. They use AI-generated code without checking licensing issues. Teams trust AI outputs without verification. Employees bypass IT policies to use unauthorized AI tools.

To tackle these challenges, some companies have already banned the use of generative AI tools. A Cisco study reveals that one in four companies has already prohibited employees from using generative AI. Over two-thirds of respondents expressed concerns about the potential risks of sensitive data being exposed to competitors or the public. These fears are well-founded, with several incidents of employee misuse resulting in harmful data leaks.

How to Manage Risks

These examples might make generative AI seem more risky than rewarding. But the reality is that organizations can't afford to sit on the sidelines —AI is becoming essential for staying competitive. The good news is that there are practical ways to manage these risks while still capturing AI's benefits. The key is developing a thoughtful approach that balances innovation with proper controls.

Risk Management: Start With Understanding

Before setting up controls, you need to know exactly how AI is being used in your organization. Most companies are surprised when they do this inventory. For example, a bank may find AI tools scattered across its operations—customer service writing emails, research teams analyzing data, and developers generating code. Each use case brings its own risks.

Building Your Monitoring System

Just tracking AI usage isn't enough. You need to spot problems before they become crises. Set up baselines for normal AI use patterns. This helps you notice when something's off—like an unusual spike in sensitive data processing or sudden changes in how teams are using AI tools.

Control Your AI Environment

One of the most effective ways to manage AI risks is to self-host your generative AI systems rather than relying on third-party APIs. While public AI tools offer convenience, they also mean losing control over how your data is processed and used. Self-hosting puts you in charge of your AI environment, letting you:

• Keep sensitive data within your infrastructure
• Implement custom security controls
• Prevent your data from being used to train models that competitors might access
• Maintain clearer audit trails
• Ensure compliance with specific regulatory requirements

Creating an Incident Response Plan

When something goes wrong with AI, you need to act fast. Your response plan should be specific and practical:

1. How to stop the immediate problem
2. Who needs to be told, and when
3. Steps to fix what went wrong
4. Ways to prevent it from happening again

If an AI system leaks sensitive data, for example, you need to know exactly how to contain it, notify affected parties, and fix the underlying issue.

Legal Framework: Beyond Basic Compliance

As AI technology and regulations evolve, your legal approach needs to keep up and cover several critical areas:

Privacy and Data Protection

Ensure AI systems comply with privacy laws like GDPR and CCPA. Know what personal data your AI systems process, how to handle data subject requests, and how to maintain proper records. Set up processes to review AI outputs for potential privacy violations before they reach customers.

Intellectual Property

Establish clear guidelines for the ownership of AI-generated content. When developers use AI coding tools, verify that the generated code is free from protected IP. Document your AI training data sources and get proper licenses for any copyrighted material you use.

Bias and Fairness

Create frameworks to test AI systems for discriminatory outcomes. Set standards for fairness in AI-driven decisions, especially in sensitive areas like hiring or lending. Document your testing methods and maintain clear records of how you address bias when found.

Vendor Management

Watch your AI vendor contracts carefully. Be clear about:

• Data handling and privacy requirements
• Model training rights and limitations
• Liability for AI mistakes or biases
• Required security measures
• Performance standards
• Audit rights

Regulatory Compliance

Track AI regulations in your industry and regions. Financial services firms need to consider SEC requirements, healthcare organizations must ensure HIPAA compliance and government contractors have their own set of rules. Build processes to demonstrate compliance and maintain proper documentation.

Keep reviewing and updating these frameworks as regulations and AI capabilities evolve.

Creating Organizational Alignment

Success with AI requires everyone in your organization to understand and support responsible AI practices. Here's how to make that happen:

Strong Governance

Create a team that oversees AI use across your organization, bringing together leaders from technology, legal, compliance, security, and business units. This team should:

• Set clear policies for AI use
• Review new AI tools and use cases
• Track how well controls are working
• Adjust strategies as risks and technology evolve

Effective Training

Don't just tell employees what they can't do - help them understand how to use AI safely and effectively. Your training should:

• Show them what "sensitive data" really means in your organization with concrete examples. 
• Explain the real consequences of data leaks—not just for the company, but for customers and employees. 
• Give them practical ways to evaluate whether something is safe to share with AI.

Most importantly, provide alternatives. If they can't use a public AI tool for sensitive work, make sure they know what tools they can use.

Smart Implementation

Make your AI policies practical and easy to follow. Consider setting up different levels of AI access based on job needs and risks. For example:

• Marketing might use approved AI tools for content ideas, but need human review before publication
• Developers could use specialized code assistants with built-in security checks
• Customer service might use AI tools that are pre-trained on approved responses

Keep checking how these policies work in practice. The best policies balance protection with productivity - they keep your organization safe while helping people do their jobs better.

Mission and Generative AI

At Mission, we understand that managing generative AI risks requires more than technical solutions—it demands a holistic approach that addresses the full spectrum of challenges organizations face. Our deep knowledge and expertise help organizations harness AI's potential while maintaining robust security and compliance.

We work with organizations to:

• Develop customized and safe AI solutions that align with business objectives
• Implement secure AI infrastructure with appropriate controls and monitoring
• Create comprehensive risk management programs that address both current and emerging threats
• Provide ongoing support and guidance as AI capabilities and risks evolve

Our approach combines deep technical expertise with practical business experience, helping organizations navigate the complex landscape of AI risk management. We focus on creating sustainable solutions that enable innovation while protecting critical assets and maintaining stakeholder trust.

Want to learn more about how we can help you manage generative AI risks while driving innovation? Learn more about Mission's generative AI services here.