Addressing security in artificial intelligence requires protecting AI systems themselves from attacks targeting models, data, and infrastructure components critically. The Artificial Intelligence (AI) in Security Market size is projected to grow USD 28.31 Billion by 2035, exhibiting a CAGR of 11.46% during the forecast period 2025-2035. As organizations deploy AI for critical functions, protecting these systems becomes essential for maintaining operational integrity. Adversarial attacks manipulate model inputs causing incorrect outputs through carefully crafted perturbations imperceptible to humans. Data poisoning compromises training datasets introducing biases or backdoors affecting model behavior in production. Model extraction attacks reverse-engineer proprietary algorithms through careful query analysis threatening intellectual property. Privacy attacks extract sensitive training data information from models raising compliance and confidentiality concerns. Supply chain risks in AI components including frameworks, libraries, and pre-trained models require careful evaluation. Securing AI systems requires specialized approaches addressing unique vulnerabilities distinct from traditional software security.

Adversarial robustness techniques strengthen models against input manipulation attacks attempting to cause misclassification or incorrect outputs. Adversarial training exposes models to attack examples during training developing resilience against perturbation attempts. Input validation filters detect and reject adversarial examples before model processing preventing manipulation. Defensive distillation reduces model sensitivity to small input changes that adversarial attacks exploit. Certified defense methods provide mathematical guarantees of robustness within defined perturbation bounds. Ensemble approaches combine multiple models making consistent adversarial manipulation more difficult for attackers. Detection mechanisms identify adversarial inputs enabling appropriate handling without model compromise occurring.

Data security protects training datasets from poisoning, theft, and unauthorized modification ensuring model integrity. Data provenance tracking maintains records of data sources, transformations, and access throughout model development pipelines. Anomaly detection identifies potentially poisoned examples within training datasets before model training begins. Differential privacy techniques protect individual data points while enabling useful model training on sensitive datasets. Secure computation methods enable model training on encrypted data protecting confidentiality throughout processes. Access controls restrict dataset access to authorized personnel preventing unauthorized modification or theft. Data validation ensures training data quality and integrity detecting manipulation attempts before damage occurs.

Infrastructure security protects AI systems throughout development, deployment, and operation against various attack vectors. Container security hardens runtime environments preventing attacks on AI workloads during execution. Model registry protection secures stored models from unauthorized access, modification, or theft. API security protects model interfaces from abuse, injection attacks, and unauthorized access attempts. Monitoring systems detect anomalous model behavior indicating potential compromise or attack in progress. Incident response procedures address AI-specific security events including model compromise and data breaches. Governance frameworks establish security requirements, accountability, and oversight for AI systems throughout organizations.

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