US law enforcement agencies, including the Federal Bureau of Investigation, Department of Homeland Security and New York Police Department, have documented artificial intelligence as a dual threat since 2021. Agency documents view AI as both a facilitator of violent extremism and a direct target for cyberattacks.
The extremism designation will likely force AI developers to build robust content moderation systems—a significant capital expense. Companies must audit algorithms, invest in human review workflows and implement safety guardrails to prevent model misuse.
The cyberattack designation signals another cost center: defending against state and criminal actors targeting proprietary model weights, training data and inference endpoints. For companies running large language models at scale, this means dedicated security infrastructure, adversarial testing and compliance audits that were previously optional.
Together, these threat classifications reshape the unit economics of AI development. Security and compliance now function as non-negotiable line items rather than bolt-on features, compressing margins for smaller players and creating a cost moat favoring well-capitalized incumbents. Companies must factor these expenses into pricing models and runway planning.
