Before appearing in Devops Live on 12-13 March, Karthik Pelluu, chief engineer Devops in the UK Ministry of Interior, participates in how artificial intelligence and machine learning (AI/ML) has formed his professional and personal life.
By working at the forefront of Devops, Cloud and Security, Karthik has witnessed a revolution in automation, cybersecurity and compliance through industries such as finance, government and law enforcement. In this discussion, it explores the role of artificial intelligence in Devsecops, cloud security, and self -healing infrastructure, while facing the main challenges about data privacy and organizational compliance.
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Why did you choose artificial intelligence and machine learning as your invention?
I have greatly affected AI and machine learning on both my professional journey and personal experience.
In my work as a Devops, Cloud and Data engineer, AI/ML played a decisive role in automation, predictive analyzes and security improvements. These technologies simplify complex tasks, improve data -based decisions, and enhance productivity through industries such as financing, government and law enforcement, as they worked closely.
Beyond work, artificial intelligence is included in our daily life. Smart assistants such as Alexa and Siri, and the recommendations that work with artificial intelligence materials on broadcasting platforms, and smart e -commerce tools, all of which are reshaping how we interact with technology. Artificial intelligence has affected my investment strategies and personal finance management, as it is compatible with my interest in financial regulations.
Can you share specific methods that AI & ML formed your business?
Artificial intelligence was a game changed in three main areas: cloud safety, financial services, and automation.
1. Amnesty International in cloud safety and Devsecops
I have worked with the security tools that operate with the same AI, such as AWS Guardddy and Azure Security Center, to discover abnormal cases and prevent electronic threats. One of the most influential use cases was the implementation of the discovery of automatic threat to secure sensitive government data. It has transformed the ability of artificial intelligence to discover and respond to threats and respond to security operations.
2. Amnesty International in Financial Services
The discovery of AI's fraud, risk assessment, and algorithm, financial security. Automated learning models help identify abnormal cases in financial transactions, improve compliance and reduce manual intervention in discovering fraudulent activity.
3. Amnesty International in automation and improvement of the cloud
Automation with artificial intelligence memory was vital in improving cost, predicing scaling, and infrastructure management. The implementation of automatic evaluation solutions in the AI-AI-Prasitses has helped improve cloud costs and customize resources, ensuring more efficiency while maintaining performance.
Looking at your experience in securing critical infrastructure, how do you see AI's safety automation that is the future of Devsecops?
Through my experience in securing critical infrastructure, I have seen directly the challenges of maintaining safe, developed and compatible cloud environments. AI's safety automation is set to convert Devsecops, with many major developments that make up the future.
1. Discover and respond to the smart threat
The tools driven by artificial intelligence allow government agencies and financial institutions to define internal threats and unauthorized access to data in the actual time. This level of automation enhances the ability of security teams to respond to the threats faster and more effective.
2. Automated compliance and risk management
Artificial intelligence constantly wipes cloud environments to detect poor formations and ensure compliance with frameworks such as GDP, NIST, ISO 27001, and financial regulations. It reduces compliance with artificial intelligence materials and imposes proactive security policies, ensuring organizational commitment.
3. Self -recovery cloud infrastructure
Artificial intelligence allows cloud environments self -recovery, as safety accidents are automatically diluted. Systems can now automatically automatically IAM roles or prevent suspicious API calls, which reduces manual security interventions.
4. AI's incidents and forensic printing
Amnesty International can analyze safety records and generate immediate forensic reports, which accelerate accidents response and reduce stopping time. AI -driving analyzes also provide visions in the actual time in Internet crime patterns, helping law enforcement agencies to fight financial fraud and digital threats.
AI's security automation is not resorted to replacing security engineers-which promotes their ability to discover and respond to threats and prevent faster and more efficiently.
Data privacy is a great concern when using artificial intelligence in public sector applications. What are the best practices that must be followed to ensure compliance?
Ensuring the privacy of data in the applications driven by artificial intelligence is very important, especially in the public sector, where sensitive information is often processed.
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Artificial intelligence models should be built while reducing data and restricting the purpose in mind. Best practices are the use of artificial data or differential privacy techniques instead of real sensitive data when training artificial intelligence models.
2. Organizational compliance
Artificial intelligence systems that deal with personal data for frameworks such as GDP and UK Data Protection Law (DPA) 2018, NIST and ISO 27001 should be composed.
3. Securing and encrypting data processing
Institutions must implement encryption from one side to a party for sensitive artificial intelligence data. The zero confidence safety model must also be applied, using IAM roles, multi -factor authentication (MFA), and retail the network to reduce access. Biometric data should be stored in encrypted cellar with strict access control elements to prevent unauthorized access.
By following best practices, institutions can balance the innovation that AI drives with strong data protection.
