Detect Threats in Real Time With AI for Cybersecurity
Detect threats in real time with AI for cybersecurity.
AI is pushing the boundaries of machine learning and information visualization, enabling security teams to detect and respond faster to cyberattacks, improve threat detection, and automate risk management processes.
Adding artificial intelligence to your cybersecurity program requires careful planning and a strong team of cybersecurity experts to implement and maintain. To maximize the benefit of your investment in AI, it’s critical to incorporate it into all aspects of your cybersecurity infrastructure, including endpoint detection and response (EDR), threat hunting, identity and access management (IAM) and encryption.
Security risks that AI poses for your organization include data poisoning, model theft and unauthorized exposure. You also need to consider the security of all AI models, including third-party components and software libraries used in their development. Threat actors can exploit these vulnerabilities in the supply chain to compromise an AI model during the development, training or deployment stages. For example, they can use attack vectors like data poisoning to modify the input data and skew results or adversarial examples to deceive the system and produce bias.
To reduce these risks, look for a unified AI platform that inventories all AI models, packages and data across your cloud environments, surfaces shadow AI and exposes vulnerable endpoints, and prioritizes real attack paths in one place. Make sure the platform explains risk in plain language and lets you control autonomy per action or blast radius, while also enforcing least privilege with just-in-time protocols.