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Attack Vectors

• Jailbreaking bypasses safety guardrails to generate malware tutorials, misinformation, illegal content, and hateful speech, transforming AI systems from assets into liabilities by tricking models into ignoring their safety training.
• Prompt injection embeds malicious instructions into legitimate-looking data like customer-uploaded documents, causing AI to process both the genuine question and the injected commands, compromising sensitive data through seemingly normal interactions.
• Model extraction involves attackers making thousands of queries to reverse engineer proprietary models, stealing intellectual property and trade secrets from companies that invested millions in training custom AI systems.
• Adversarial attacks exploit mathematical vulnerabilities in neural networks through imperceptible changes like 1% pixel modifications to input data, causing AI to make completely wrong predictions despite the tiny alterations.

Defense Architecture

• AI security requires three control types at all layers: preventive (input validation filtering malicious prompts), detective (monitoring suspicious activity), and responsive (incident response plans with automatic service shutdown and model rollback).
• Red teaming deploys authorized security experts to deliberately break AI systems through adversarial prompting, security testing of cloud infrastructure and APIs, and risk assessment of bias and legal issues before production deployment.
• Monitoring and logging every AI interaction, including user, input, and output, enables detection of suspicious patterns like sudden query spikes or abrupt behavioral changes, critical for identifying attacks in progress.

Emerging Threats

• Multimodal attacks chain multiple models in sequence—generating a jailbreak prompt, feeding it to GPT to extract data, then weaponizing output in another model—creating compound vulnerabilities across AI systems.
• AI-powered phishing combines deepfake videos of executives with voice-cloned bots to create ultra-convincing social engineering attacks that make detecting and preventing unauthorized actions like fund transfers increasingly difficult.

Governance Frameworks

• NIST framework (govern, map, measure, manage) and ISO 42001 (first global AI management system standard) provide structured approaches to building trust, managing risk, and ensuring compliance across the AI lifecycle.
• AI security demands cross-functional teams meeting regularly to make decisions on deployments, risk tolerance, usage policies, and ongoing audits, ensuring compliance with regulations like GDPR and HIPAA as a shared organizational responsibility.

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