AI Security Risks 15 articles

Overview AI availability and denial-of-service risks pertain to threats that impact the accessibility and operational continuity of AI-driven systems, particularly those integrated into security operations and automation workflows. Ensuring uninterrupted…

Overview The AI model deployment attack surface encompasses the various points of exposure and vulnerability that arise when artificial intelligence models are integrated into operational environments. As AI-driven systems become…

Overview AI Security Risk Assessments involve the systematic evaluation of vulnerabilities, threats, and potential impacts associated with deploying artificial intelligence systems within security operations and automated environments. These assessments are…

Overview AI supply chain and dependency risks refer to vulnerabilities and threats arising from the complex ecosystem of hardware, software, data, and services that underpin AI-driven systems. These risks are…

Overview Bias, drift, and integrity failures represent critical risk areas in AI-driven systems, particularly within cybersecurity and automation contexts. These phenomena can degrade the reliability and trustworthiness of AI models,…

Overview Cloud AI platforms provide scalable infrastructure and tools for developing, deploying, and managing artificial intelligence models and applications. These platforms are integral to modern security operations by enabling automation,…

Overview Inference abuse and model misuse refer to the exploitation or improper application of AI models during their inference phase, where models generate outputs based on input data. In modern…

Overview Insider threats in AI development refer to risks posed by individuals within an organization who have authorized access to AI systems, data, or development environments and misuse this access…

Overview Model hallucinations refer to instances where artificial intelligence models, particularly large language models (LLMs), generate outputs that are plausible but factually incorrect or fabricated. In modern security operations, these…

Overview Model theft and intellectual property (IP) risks pertain to the unauthorized extraction, replication, or misuse of AI models and their proprietary components. In modern security operations, these risks undermine…

Overview Over-reliance on AI decision-making refers to the excessive dependence on automated systems and algorithms to make critical security and operational decisions without adequate human oversight. In modern security operations…

Overview Prompt injection and prompt manipulation represent security risks in AI-driven systems, particularly those utilizing large language models (LLMs) and automated conversational agents. These techniques involve adversaries crafting inputs that…

Overview Residual risk management for AI systems addresses the remaining security, privacy, and operational risks after implementing primary controls in AI-driven environments. As AI technologies increasingly integrate into automated security…

Overview Shadow AI refers to the unauthorized use or deployment of artificial intelligence models outside of established governance frameworks within an organization. This phenomenon poses significant challenges to modern security…

Overview Training data exposure and leakage refer to the unintended disclosure or compromise of datasets used to train AI models, particularly in large language models and automated systems. This risk…