The Rise of AI in Business and Its Security Implications
Organizations today are increasingly leveraging AI-powered technologies to streamline workflows, analyze and summarize data, and generate content more efficiently than ever before. You and your colleagues may already be using AI tools for research, content generation, and software development.
However, while AI can enhance business efficiency, it also introduces security concerns. A complete ban on AI technologies may not be practical, but unrestricted adoption can lead to data breaches, misinformation, and compliance risks. The key to balancing AI innovation with security is to implement a robust AI security policy.
A strong AI security policy allows organizations to harness AI benefits while ensuring the necessary safeguards are in place to protect sensitive data and ensure accuracy. Additionally, organizations must extend these security controls to their vendors, suppliers, and third-party partners, ensuring that AI-related risks are managed effectively across the entire ecosystem.
In this article, we will explore the components of an AI security policy, discuss how such policies apply to third-party risk management, and outline key questions to assess vendor AI security controls.
What Is an AI Security Policy?
An AI security policy is a structured set of guidelines that organizations use to evaluate and regulate AI tools and frameworks. These policies help organizations maximize AI’s benefits while ensuring control over data protection, security, and compliance. A well-implemented AI security policy typically includes provisions for:
- Protecting sensitive data through encryption and access control mechanisms
- Ensuring authorized access via authentication protocols
- Maintaining network security through firewalls, intrusion detection systems, and continuous monitoring
- Mitigating AI vulnerabilities such as bias, inaccuracies, and unintended outputs (AI hallucinations)
AI security policies serve as an extension of general information security policies, incorporating elements of data protection, privacy, and accuracy to safeguard AI adoption.
Key Components of an AI Security Policy
1. Tool Evaluation Policies
Organizations must establish procedures for evaluating AI tools before deployment. These policies define workflows for security teams to assess AI solutions, ensuring that sensitive data is handled appropriately and securely.
2. Source Code Security Policies
AI models often rely on proprietary or third-party code. Security policies must ensure adherence to secure coding practices, regular code reviews, and strict monitoring to prevent unauthorized access or tampering.
3. Incident Response and Risk Mitigation Policies
Organizations must implement clear incident response protocols for AI-related security breaches. These policies should align with industry standards and include remediation strategies to address AI-driven security threats.
4. Data Retention and Privacy Guidelines
AI models often require large datasets for training. Organizations must establish policies that dictate how long AI-generated data can be retained, how it should be protected, and how it complies with regional data privacy laws.
5. Ethical AI Use and Bias Prevention
AI policies should incorporate ethical considerations to ensure fairness, prevent discrimination, and reduce biases in AI-generated outputs. Regular audits and human oversight must be mandated to review AI decisions and recommendations.
6. AI Hallucination and Inaccuracy Mitigation
AI-generated content can sometimes include false, misleading, or offensive information, known as “AI hallucinations.” Organizations must acknowledge these risks and implement review mechanisms to validate AI-generated outputs before using them in decision-making processes.
Applying AI Security Policies to Third-Party Risk Management
Organizations increasingly rely on third-party vendors for AI-powered solutions. While these vendors provide valuable services, they also introduce potential security vulnerabilities. It is crucial to assess and mitigate AI risks associated with third-party vendors through structured policies.
Key Areas Where AI Security Policies Apply to Third-Party Risk Management:
- Pre-Contract Due Diligence – Organizations should assess vendors’ AI security controls before signing agreements to ensure that they align with internal security standards.
- Vendor Contracting – AI security requirements should be incorporated into contracts to enforce data protection, access controls, and incident response obligations.
- Ongoing Vendor Assessments – Continuous monitoring of vendors ensures compliance with evolving AI security standards and helps identify vulnerabilities before they become critical risks.
AI Security Controls Assessment: 16 Critical Questions for Third Parties
Organizations must proactively assess the security posture of their AI vendors. The following questionnaire, based on industry best practices, serves as a guide for evaluating AI security risks in third-party relationships:
Data Protection and Privacy
- Is AI training data collected only from trusted sources?
- Has the vendor implemented a formal data policy that includes data classification and privacy protection?
- Are secure storage and access control measures applied to all datasets?
Model Security and Integrity
- Are datasets verified via cryptographic hashing before use?
- Does the vendor track dataset integrity throughout the AI system lifecycle?
- Are AI model training environments secured and monitored?
AI Deployment and Maintenance
- Are AI models tested in conditions that match real-world deployment scenarios?
- Is AI model code reviewed in dedicated, secure environments before deployment?
- Are AI systems regularly updated and retrained to mitigate risks of outdated models?
Network and Infrastructure Security
- Does the vendor ensure network segmentation and secure AI system access controls?
- Are event logs and security monitoring tools used to track AI system activity?
- Are AI security incidents promptly reported and addressed?
Business Continuity and Risk Management
- Does the vendor have an AI-specific incident response plan?
- Are AI system failures accounted for in business continuity planning?
- Are contingency measures in place to recover from AI-related security incidents?
- Are third-party AI solutions regularly reviewed for compliance with industry standards?
Next Steps for Managing Third-Party AI Risks
The above questionnaire is an essential tool for organizations to identify AI security risks in third-party partnerships. Conducting regular AI security assessments ensures compliance, enhances data protection, and mitigates risks associated with AI-generated misinformation or biased decision-making.
Organizations that proactively manage third-party AI risks strengthen their cybersecurity posture, maintain regulatory compliance, and build trust with stakeholders.
Get Started with Third-Party Risk Management on Connected Risk
AI adoption is accelerating, but security risks should not be overlooked. Implementing a comprehensive AI security policy and enforcing it across third-party vendors is critical to protecting your organization’s data and operations.
Leverage Connected Risk to streamline your Third-Party Risk Management (TPRM) process and gain greater visibility into AI security risks across your vendor ecosystem. Contact us today to learn how our platform can help you proactively manage AI-driven risks and enhance security compliance for your business.
