Leveraging cyber security for Artificial Intelligence 


By Jesintha Louis, Chief Executive Officer, G7 CR Technologies India Pvt Ltd

 In the digital age, SaaS businesses have started embracing transformative technologies, such as Artificial intelligence (AI) and cloud computing. According to a research firm, the market for artificial intelligence (AI) is nearly 100 billion USD, which is expected to grow twentyfold by 2030, up to almost 2 trillion USD.

 Although AI promises revolutionary advancements and cloud computing enables efficient storage and processing of massive amounts of data, their rapid adoption also raises concerns about cybersecurity. In 2021, the global cost of cybercrime was estimated to be $6 trillion.

As tech companies continue to intersect cloud computing and AI technology, leveraging cyber security becomes crucial to ensuring the integrity, confidentiality, and availability of these cutting-edge technologies. With effective cyber security practices to safeguard sensitive data, algorithms, and infrastructures, SaaS companies can comprehend the intricate interplay between cyber security, AI, and cloud computing to establish a robust framework that mitigates vulnerabilities.

AI Product Companies need to understand that cybersecurity is an ongoing process and is more critical with the advent of generative AI. Staying informed about emerging threats and security best practices in the AI domain and adapting strategies accordingly will help in leveraging cyber security. SaaS companies need to regularly review and update their security measures as well in order to protect their AI apps from evolving cyber threats.

To combat security issues in this new era of generative AI and metaverse, cloud managed service providers like G7 CR Technologies are helping companies get ready for the next transformation, leveraging Microsoft Security Solutions like Copilot to detect hidden patterns, harden defenses, and respond to incidents faster with generative AI. Microsoft Copilot simplifies the complexities of data into clear and actionable insights to respond to incidents in minutes instead of hours or days. Also, it provides advanced insights into possible security threats from the attacker and guides through the next steps for mitigating the cybersecurity risks.

Talking about leveraging cyber security for artificial intelligence (AI), it is closely related to cloud computing due to the significant role that cloud platforms play in hosting and supporting AI systems and infrastructures. Therefore, it becomes essential to utilize the power of cloud computing, which promotes the responsible and secure use of such technologies.

The following are the steps to consider cyber security for AI.

Data Security: Cloud storage and processing capabilities are often utilized to handle large volumes of data required for training AI models. In such scenarios, SaaS companies can use strong encryption techniques to protect the AI application data. Ensure that sensitive data, such as user information or proprietary algorithms, is encrypted both at rest and in transit. It is recommended to implement access controls as well as authentication mechanisms to restrict data access to unauthorized individuals or systems.

Secure Development Lifecycle: While building the AI-based SaaS product, businesses can implement a secure mechanism for the development process that will include security considerations at every stage of the AI product’s lifecycle. The process involves threat modeling, secure coding practices, regular software testing, and code reviews to detect and remove vulnerabilities early on. Also, cloud computing provides the underlying infrastructure and resources required to develop, train, and deploy AI models.

Multi-factor Authentication and Authorization: Implement strong authentication mechanisms across the AI-based product to ensure the real-time verification of users’ and systems’ identities that are accessing the product. Azure AD Multi-Factor Authentication (MFA) is also considered for additional security which requires users to enter more information than just a password. Use Identity and Access Management (IAM) policies that also enable granular control over permissions, limiting access privileges based on user roles or responsibilities.

Regular Updates and Patching: To leverage cyber security for AI, it is essential for SaaS companies to stay up to date with security patches and software updates for all components of the AI-based product or application. This will include updating operating systems, frameworks, libraries, and dependencies to ensure there is no vulnerability, and if found, that should be mitigated immediately. Also, cloud computing brings regulatory considerations and compliance requirements. Therefore, it is essential to ensure that the use of cloud services for AI aligns with relevant data protection regulations and industry-specific compliance standards.

Monitoring and Logging: Cloud-based AI systems can benefit from the security monitoring and threat detection capabilities offered by cloud platforms. By implementing comprehensive logging mechanisms, product owners can track and record activities within their AI applications to monitor for unusual behavior, security incidents, anomalous activities, and potential cyber threats and respond to security events in real-time. Cloud solution providers like Microsoft Azure offer such services as Azure Monitor Logs that aid in proactive threat monitoring.

Cloud managed service providers like G7 CR has strong technical and consultative capabilities around Data & AI, Infrastructure, Digital & App Innovation, and Modern Work & Security to help organizations strengthen and safeguard their software and applications leveraging AI-powered cyber security solutions from Microsoft. Prioritizing the safety and security of SMEs and SMBs from various sectors like banking, manufacturing, retail, eCommerce, etc., we can constantly evaluate and enhance the approach to cyber security and ensure systems and data are protected for clients globally. 


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