Generative artificial intelligence (Gen AI) has placed intellectual property (IP) law by affecting the entire chain of creative, technical, and commercial production. Copyright law must determine the extent of human control required to protect AI-assisted work; whether extensive ingestion of protected works for model training constitutes infringement, fair use, text and data-mining, or licensed use; and how courts should evaluate outputs that resemble or compete with existing works. Patent law confronts a related question: how to preserve human inventorship as generative technologies increasingly assist with hypothesis formation, molecular design, code generation, engineering optimization, claim drafting, and experimental planning. Trademark law is also changing, although it has received less attention. In AI-mediated markets, consumers may no longer encounter trademarks through ordinary searches and comparisons. Instead, they may receive ranked answers, conversational recommendations, generated product descriptions, synthetic endorsements, or automated purchase suggestions. Trade secret law has become equally central because model weights, datasets, prompts, safety procedures, filtering methods, benchmarks, and fine-tuning practices are often protected as secrets, even when creators, regulators, courts, and downstream users require additional information to evaluate legal compliance and public risk. This study argues that generative AI should be regulated by reconstructing existing intellectual property principles rather than granting legal personhood to AI systems or creating new, exclusive rights for machine-generated outputs. Human authorship and inventorship should remain the legal baseline. Meanwhile, the law must exercise greater caution regarding the stages at which AI creates legal risks. Training, model development, output generation, and market deployment raise different questions and should not be reduced to a singular debate about AI and IP. This study employs doctrinal, comparative, and policy analyses using materials from the U.S., EU, U.K., and international organizations. It examines recent case law and administrative guidance alongside academic scholarship on authorship, fair use, text and data mining, augmented inventorship, algorithmic trademarks, deepfakes, transparency, and trade secrets. It concludes that the most affective AI era framework is layered and emphasizes human-centered attribution, contextual training approaches, meaningful transparency, sector-sensitive licensing, output-stage accountability, disciplined trade secret protection, and coordination among IP law, competition law, consumer protection, privacy, and AI regulation.