Prepare for AI-accelerated attacks
As attackers use AI to scale operations, security leaders must use AI to proactively secure their people, data, and infrastructure. Explore IBM’s X-Force Threat Intelligence Index to learn about the challenges and successes experienced by security teams around the world.
Year-over-year increase in the exploitation of public facing software or system applications
Share of disclosed vulnerabilities that did not require authentication to successfully exploit
Number of AI chatbot credentials observed for sale on the dark web
Increase in active ransomware groups compared to the prior year
Take action
The recent rise of autonomous security operations centers, which use agentic AI to augment the roles of security workers, can orchestrate multiple agents to work across the entire threat lifecycle—from threat hunting to remediation. That can give organizations the tools they need to detect and mitigate increasingly complex AI-generated attacks.
As attackers fine-tune their credential‑driven operations, security leaders must turn to AI gain visibility into identity-based risks and threats. By combining AI-powered identity threat detection and response (ITDR) and identity security posture management (ISPM), they can more quickly and efficiently identify vulnerabilities and prevent attacks from happening.
You need to know about and address vulnerabilities before attackers do. To get there, you should adopt a continuous, proactive approach to identifying weaknesses across your environment, including secure code review, weak or reused credentials, misconfigurations and dynamic testing and scanning across on‑premises and cloud environments to detect missing patches.
Agentic AI has introduced new risks and amplified old ones. As a result, AI needs security and governance solutions to scale with trust and transparency. Organizations should enforce strong AI authentication and access controls, protect AI service credentials and monitor for abnormal access patterns. Model governance is needed to evaluate underlying AI, test performance for bias, drift, and accuracy and safeguard against inappropriate behavior.
While data security has always been important, it gains even more importance in the AI world—discovering where the sensitive data lives, understanding how it’s used and exposed, prioritizing data risks based on context—all while meeting the stringent compliance requirements of dozens of global and regional regulatory agencies.
The recent rise of autonomous security operations centers, which use agentic AI to augment the roles of security workers, can orchestrate multiple agents to work across the entire threat lifecycle—from threat hunting to remediation. That can give organizations the tools they need to detect and mitigate increasingly complex AI-generated attacks.
As attackers fine-tune their credential‑driven operations, security leaders must turn to AI gain visibility into identity-based risks and threats. By combining AI-powered identity threat detection and response (ITDR) and identity security posture management (ISPM), they can more quickly and efficiently identify vulnerabilities and prevent attacks from happening.
You need to know about and address vulnerabilities before attackers do. To get there, you should adopt a continuous, proactive approach to identifying weaknesses across your environment, including secure code review, weak or reused credentials, misconfigurations and dynamic testing and scanning across on‑premises and cloud environments to detect missing patches.
Agentic AI has introduced new risks and amplified old ones. As a result, AI needs security and governance solutions to scale with trust and transparency. Organizations should enforce strong AI authentication and access controls, protect AI service credentials and monitor for abnormal access patterns. Model governance is needed to evaluate underlying AI, test performance for bias, drift, and accuracy and safeguard against inappropriate behavior.
While data security has always been important, it gains even more importance in the AI world—discovering where the sensitive data lives, understanding how it’s used and exposed, prioritizing data risks based on context—all while meeting the stringent compliance requirements of dozens of global and regional regulatory agencies.