4 Cybersecurity Considerations for AI Deployment

Focus areas and preparations to help you achieve successful in-house AI readiness

As the world of business continues to evolve, innovative technologies like Artificial Intelligence (AI) are quickly becoming critical components of modern commerce. The ability to harness the power of AI is quickly separating the winners from the losers, making it essential for companies to understand how to properly secure, govern and maintain compliance with this transformative technology. 

For companies looking to drive efficiency, productivity, innovation and other critical business outcomes, AI is the clear choice. But before you can take advantage of AI's many benefits, you need to have a comprehensive understanding of your technological landscape and how it can be optimized for AI. From cloud infrastructure and cybersecurity to data management and applications, there are many factors to consider when becoming AI-ready. 

To ensure that your company is fully prepared for AI, you need to take a proactive approach to cybersecurity, governance, compliance and data protection.

Only by working diligently to secure your AI infrastructure, optimize your governance framework, ensure regulatory compliance and protect your data can you fully leverage the power of AI to take your business to the next level. 

Preparing for AI Deployment

As AI becomes increasingly integrated into business operations, companies must prioritize cybersecurity, governance, compliance and data protection to ensure successful in-house AI readiness. Below, you can find where organizations need to focus within these areas to prepare for in-house AI deployment. 

1. Cybersecurity  

In-house AI readiness requires robust cybersecurity measures to protect sensitive data and AI systems from cyberattacks. Companies should focus on the following cybersecurity measures: 

• Endpoint security: Endpoint security solutions, such as antivirus software and firewalls, can protect AI systems and data from unauthorized access and cyber threats. 
• Secure communications: Secure communications protocols, such as SSL/TLS encryption and virtual private networks (VPNs), can protect data in transit between AI systems and other endpoints. 
• Data encryption: Encryption can protect sensitive data from unauthorized access by encrypting data at rest and in transit. 
• Vulnerability management: Regular vulnerability assessments and penetration testing can help companies identify and remediate vulnerabilities in AI systems and data. 

2. Compliance  

Compliance is essential to ensure that AI systems are developed and used in compliance with applicable laws and regulations. Companies should focus on the following compliance measures: 

• Policy review:  HR and InfoSec policies should be reviewed and updated to reflect ChatGPT specific use case enablement and management. 
• GPT cost management: procedures and methods should be reviewed to ensure cost allocation and budgets are in place.  
• Regulatory: Companies must ensure that their AI systems comply with applicable regulations, such as GDPR, CCPA and HIPAA. 
• Auditing and reporting: Companies should maintain audit trails and reporting processes to demonstrate compliance with regulations and internal policies. 

3. Data Protection  

Data protection is critical to ensure that sensitive data used by AI systems is secure and protected. Companies should focus on the following data protection measures: 

• Data classification and access control: Companies should classify data based on its sensitivity and restrict access to sensitive data to authorized personnel only. 
• Data encryption: Data encryption can protect sensitive data from unauthorized access. 
• Data retention and disposal: Companies should establish policies and procedures for data retention and disposal to ensure that sensitive data is securely disposed of when no longer needed.

4. Data Governance  

Data Governance is an essential consideration when deploying GPT. It refers to the overall management of the availability, usability, integrity, and security of the data used in an organization. It is essential to ensure that data is collected, stored, used, and disposed of ethically, legally, and effectively. 

Companies should focus on the following data governance measures: 

• Data classification policies: These policies define how data is classified based on its level of sensitivity, criticality, and value to the organization. This classification helps organizations understand how to manage and protect their data. 
• Data retention and disposal policies: These policies specify how long data is kept, when it should be disposed of, and how it should be destroyed to prevent unauthorized access. 
• Data access policies: These policies determine who has access to data and under what circumstances. This includes policies for granting and revoking access, and procedures for ensuring that access is granted only to authorized personnel. 
• Data privacy policies: These policies outline how data is collected, used, and shared to protect the privacy and confidentiality of individuals. This includes policies for obtaining consent, anonymizing data, and ensuring compliance with relevant regulations such as GDPR and CCPA. 

To truly leverage the power of AI, companies must take a comprehensive approach to cybersecurity, governance, compliance and data protection. By focusing on these critical areas, companies can ensure that their AI infrastructure is secure, optimized, and fully compliant, while also leveraging the full power of this transformative technology to drive innovation, productivity and growth.