Executive Development Programme in Smart Grid Threat Mitigation
-- ViewingNowThe Executive Development Programme in Smart Grid Threat Mitigation is a certificate course designed to address the growing need for cybersecurity expertise in the smart grid industry. With the increasing adoption of smart grids, the demand for professionals who can mitigate threats and ensure system security has never been higher.
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โข Introduction to Smart Grids: Understanding the fundamentals of smart grids, including architecture, components, and functionalities.
โข Smart Grid Cybersecurity Threats: Identifying and categorizing common cybersecurity threats to smart grids, such as unauthorized access, data breaches, and denial-of-service attacks.
โข Risk Management in Smart Grids: Implementing risk management strategies to protect smart grids from cybersecurity threats, including threat modeling, risk assessment, and mitigation techniques.
โข Security Standards and Regulations: Complying with relevant security standards and regulations, such as NERC-CIP, NIST, and IEC 62351, to ensure smart grid security.
โข Cryptography and Network Security: Utilizing cryptography techniques and network security measures, such as encryption, authentication, and access control, to protect smart grid communications and data.
โข Incident Response and Disaster Recovery: Developing incident response plans and disaster recovery strategies to minimize the impact of cybersecurity incidents on smart grids and ensure business continuity.
โข Physical Security for Smart Grids: Implementing physical security measures, such as access control, surveillance, and perimeter security, to protect smart grid infrastructure and assets.
โข Security Awareness and Training: Providing security awareness training and education to smart grid personnel to ensure a culture of security and reduce human error.
โข Emerging Trends and Future Challenges: Staying up-to-date with emerging trends and future challenges in smart grid security, such as the impact of artificial intelligence and machine learning, and preparing for them through proactive security measures.
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