Leading the Way in AI Cybersecurity
As AI becomes increasingly woven into a variety of systems, leaders need to think about the security of these emerging technologies. The NCSC has developed a set of guidelines for secure AI system development to help stakeholders deliver the right outcomes through a combination of design, implementation and operation of AI software.
Better threat detection and response: AI is able to collect vast data volumes, sift through them, ingest the latest intelligence about threats and then immediately take action across large IT environments. This enables it to detect anomalous behavior and prevents attacks in real time while improving situational awareness and decision-making for human security personnel.
Detecting insider threats: AI is able to automatically sift through vast amounts of data and identify patterns that indicate unauthorized access, theft or exfiltration. It can also reduce incident response times and work around the clock to stop threats, even when a team is offline.
It is important to remember that AI cybersecurity measures are as much about organisational culture and process as they are technical. Leaders must build security into all AI projects and workflows from the beginning, including embedding security as part of the project management process. They should also have a clear incident response plan that covers containment, investigation and remediation of any cyber attacks involving AI. Additionally, managers should be familiar with legal considerations for AI and privacy issues such as the potential for malicious actors to manipulate AI models to produce unintended results.