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Privacy concerns how laws and norms govern personal data, shaping cybersecurity duties for collection, storage, access, and disclosure.

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Background for this topic.

Privacy is the ability of people to control how information about them is collected, used, retained, and disclosed. In technical and legal contexts, it covers identifiable data and data that can support inferences about a person, not only information made public. Privacy rules and organizational policies commonly address purpose, transparency, access, correction, retention, and sharing.

For security practitioners, privacy depends on reducing unnecessary data and restricting legitimate access: data minimization, encryption, least-privilege controls, segregation of identifiers, retention limits, and audit logs all reduce exposure. Compromised credentials, misconfigured storage, excessive telemetry, or third-party access can reveal sensitive information; pseudonymized datasets may also be re-identified when combined with other data. During an incident, teams must establish what personal data was accessed or disclosed, contain further exposure, preserve evidence, and meet applicable notification and handling requirements.

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Most people are barely thinking about basic cybersecurity, let alone post-quantum cryptography. But the impact of a post-quantum world is coming for them regardless of whether or not it's keeping them up tonight.  Today, many rely on encryption in their daily lives to protect their fundamental digital privacy and security, whether for messaging friends and family, storing files and photos, or

Generative AI is a double-edged sword, if there ever was one. There is broad agreement that tools like ChatGPT are unleashing waves of productivity across the business, from IT, to customer experience, to engineering. That's on the one hand.  On the other end of this fencing match: risk. From IP leakage and data privacy risks to the empowering of cybercriminals with AI tools, generative AI