What Is AI Security?

AI security refers to both the tools, technologies, and security measures organizations use to secure their AI stack as well as the use of AI to augment cybersecurity systems to improve vulnerability detection, correlations and response actions moving your security operations teams from a reactive to a proactive security posture. Among other benefits, AI-driven cybersecurity applications can significantly enhance the reach and effectiveness of an organization’s cybersecurity defenses by automating their threat detection and incidence response activities, carrying out vulnerability scans and other proactive measures on a regular or ongoing basis, and using the latest threat intelligence and security analytics to predict, pre-empt, and protect organizations from both new and emerging cyber threats. Some of the most effective and widely adopted applications of AI cybersecurity include the use of artificial intelligence in data protection, endpoint security, cloud security, advanced threat hunting, fraud detection, and identity and access management (IAM). Trend Vision One features a powerful set of industry-leading AI tools and technologies that can detect, predict, and prevent cyber threats far more rapidly and effectively than traditional human-led security teams.

网络安全行业深度分析

1. What Is AI Security? – IBM

Short for artificial intelligence (AI) security, AI security is the process of using AI to enhance an organization’s security posture. With AI systems, organizations can automate threat detection, prevention and remediation to better combat cyberattacks and data breaches. According to the IBM*Cost of a Data Breach* Report, organizations with extensive security AI and automation identified and contained data breaches 108 days faster on average than organizations without AI tools. Also, the report found that organizations that extensively use AI security save, on average, USD 1.76 million on the costs of responding to data breaches. While the focus of this page is the use of AI to improve cybersecurity, two other common definitions center on securing AI models and programs from malicious use or unauthorized access. Being a relatively new technology, AI models also provide threat actors with new opportunities for cyberattacks, such as supply chain attacks and adversarial attacks (see “Potential vulnerabilities and security risks of AI”).

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2. What Is AI Security? [Protecting Models, Data, and Trust] – Palo Alto Networks

AI security is the discipline of protecting artificial intelligence systems from threats that compromise their integrity, confidentiality, or reliability.

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3. What is AI Security? – Check Point Software

# What is AI Security? On the one hand, AI introduces significant new security risks to sensitive corporate and customer data. ## AI Security Risks. Because AI can be implemented in drastically different formats according to the data it’s trained on, the following use cases are grouped according to the security tools implementing the AI. ### AI In Network Security. ### AI in Endpoint Security. For example, AI can monitor network traffic and look for traffic surges or unusual communication patterns that could indicate a [DDoS attack](/cyber-hub/cyber-security/what-is-ddos/) or lateral movement by malware. AI aids security analysts by automating the integration and analysis of threat data. * **Improved Security Visibility:** AI can analyze large volumes of data and extract useful insights and threat intelligence. * **Implement AI Security Policies:** Cyber threat actors may target AI systems in their attacks. Implement security policies and controls to protect AI training data and models against potential exploitation.

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4. Top 8 AI Security Best Practices – Sysdig

The adoption of AI is accelerating across industries, from healthcare to finance, and this proliferation introduces both opportunities and challenges. With AI systems becoming integral to decision-making processes, IT professionals must address vulnerabilities that could compromise the integrity and functionality of these systems. ## Risks in AI security. Unlike traditional cybersecurity, which primarily focuses on protecting networks, systems, and data, AI security also involves mitigating risks unique to AI systems. * **Data poisoning:** Data poisoning occurs when attackers introduce malicious data into an AI system’s training pipeline, compromising the model’s reliability and accuracy. Encryption, access controls, and usage monitoring can help to prevent theft. * **Supply chain risks:** AI systems depend on a vast web of third-party tools and data, introducing a plethora of potential vulnerabilities and potential points of failure. * **API vulnerabilities:** APIs are critical for accessing AI functionality but can expose systems to data theft and injection attacks if poorly secured. Monitoring and anomaly detection can help to mitigate this risk.

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