4 Cybersecurity Considerations for AI Deployment
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.
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