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Application security professionals are adapting their strategies to address the unique risks introduced by artificial intelligence (AI) systems. Traditional AppSec measures are no longer sufficient, as AI models present novel vulnerabilities such as training data poisoning, excessive autonomy, and the use of malicious or low-reputation models. Organizations are urged to implement AI-specific security testing frameworks, monitor for anomalous model behaviors, and leverage resources like the OWASP LLM & Gen AI Top 10 to identify and mitigate these emerging threats.
The rise of agentic AI and non-human identities (NHIs) further complicates the security landscape, requiring robust management of machine identities, encrypted secrets, and permissions. Effective NHI management involves continuous discovery, classification, and monitoring to detect threats and remediate risks, especially as organizations increasingly rely on automated processes for critical system operations. The disconnect between security and R&D teams is highlighted as a significant challenge, underscoring the need for cross-functional collaboration to secure AI-driven environments.
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