Secure AI model deployment is the process of making artificial intelligence models available for use while ensuring they are protected from cyber threats and misuse. It involves safeguarding the model, the data it uses, and the systems that run it. This helps maintain privacy, trust, and reliability when AI solutions are put into operation.
Category: Cybersecurity
Secure Data Collaboration
Secure data collaboration allows people or organisations to work together on shared data while keeping that data safe from unauthorised access. It uses technology and rules to protect sensitive information, ensuring only approved users can view or change data. This is important when teams from different companies or departments need to cooperate but must follow…
Cloud-Native Application Security
Cloud-native application security is the practice of protecting software that is designed to run in cloud environments. These applications are often built using containers, microservices, and managed services, which require different security measures than traditional software. The goal is to keep data safe, prevent unauthorised access, and ensure the software works as intended even as…
AI for Fraud Detection
AI for Fraud Detection uses computer systems to automatically spot suspicious or dishonest activity, such as unauthorised transactions or false information. By analysing large amounts of data, AI can recognise patterns and behaviours that might indicate fraud. This helps organisations respond quickly and prevent losses.
Cloud Workload Security
Cloud workload security refers to protecting applications, data, and processes that run in cloud environments. It involves securing the different elements of a workload, such as virtual machines, containers, and serverless functions, from threats and unauthorised access. This is achieved through monitoring, access controls, vulnerability management, and automated responses to suspicious activity.
Secure Multi-Party Analytics
Secure Multi-Party Analytics is a method that allows several organisations or individuals to analyse shared data together without revealing their private information to each other. It uses cryptographic techniques to ensure that each party’s data remains confidential during analysis. This approach enables valuable insights to be gained from combined data sets while respecting privacy and…
AI-Driven Threat Intelligence
AI-driven threat intelligence uses artificial intelligence to automatically collect, analyse, and interpret information about potential cyber threats. This technology helps security teams quickly identify new risks, suspicious activities, and attacks by scanning vast amounts of data from multiple sources. By using AI, organisations can respond faster to threats and reduce the chances of security breaches.
Smart Contract Auditing
Smart contract auditing is the process of reviewing and analysing the code of a smart contract to find errors, security vulnerabilities, and potential risks before it is deployed on a blockchain. Auditors use a mix of automated tools and manual checks to ensure the contract works as intended and cannot be exploited. This helps protect…
AI for Cybersecurity Analytics
AI for Cybersecurity Analytics refers to the use of artificial intelligence techniques to detect, analyse, and respond to digital security threats. By processing large volumes of data from networks, systems, and devices, AI can identify unusual patterns or behaviours that might indicate cyber attacks. These systems can automate threat detection and response, helping organisations protect…
Quantum-Safe Cryptography
Quantum-safe cryptography refers to encryption methods designed to remain secure even if powerful quantum computers become available. Traditional encryption could be broken by quantum computers, so new algorithms are being developed to protect sensitive information. These methods aim to ensure that data remains confidential and secure both now and in the future, even against advanced…