Grand Theft Liability: Autonomous AI and the Risk Landscape in Media and Advertising

The next phase of gaming and media is content fully created by Artificial Intelligence (AI). In a matter of months, generative AI has shifted from a niche innovation to a core engine behind content development in gaming, film, and advertising. The evolution is no longer assistive AI that enhances creative workflows, it’s fully autonomous AI systems that independently create, adapt, and deploy gaming and media content at scale. These tools aren’t just rewriting scripts; they’re reshaping the liability map of the entertainment and advertising industries.

Companies are rapidly deploying AI to generate virtual actors, dynamic ad copy, procedurally built game worlds, and personalized storylines, sometimes with minimal to zero human review. But this new frontier in content creation carries with it new forms of legal, financial, and reputational risk.

What happens when AI content defames a real person, incites violence, deepfakes a celebrity, or violates IP rights? Who gets sued? Who pays? And are insurers ready to cover the fallout?

Autonomous AI: From Tool to Creator

Fully autonomous AI content systems operate beyond traditional prompt-and-response models. Instead, they integrate:

• Data ingestion: AI consumes vast datasets from public and proprietary sources
• Autonomous decision-making: The system interprets, decides, and generates media without human approval
• Content deployment: Final assets are published directly into games, films, or ad platforms with minimal oversight

This closed-loop process is already being applied in:

• Film: AI systems write dialogue, score music, generate synthetic actors, and edit footage in real time.
• Advertising: AI ad engines generate custom visuals, taglines, and campaign variants, adapting dynamically to user behavior.
• Video Games: Entire interactive narratives, characters, and in-game decisions are autonomously built and deployed.

The benefit? Speed, personalization, and scalability. The risk? Unpredictable outputs with potentially serious consequences.

Legal Risk: Liability in a Lawless Landscape

U.S. liability frameworks were never built to address content authored by machines. While software has long played a role in content development, the difference with autonomous AI is the absence of human editorial review. This undermines traditional assumptions about fault, intent, and due diligence.

Here are five legal frameworks now being tested:

• Defamation

If an AI creates a character who clearly resembles a real person (in appearance, name, or behavior) and presents them in a harmful or false light, that could constitute defamation. But who authored the defamatory content? The studio using the AI? The developer of the model? The player in the video game? Or the AI itself?

No court has yet ruled decisively on this. But most experts agree that responsibility will fall to the party with effective control over deployment, typically the company using the AI, regardless of whether they intended harm.

• Negligence and Duty of Care

If a company deploys an AI system that generates offensive or unlawful content, have they failed in their duty of care? Plaintiffs may argue that failing to test, constrain, or monitor generative models constitutes negligence, particularly in high-risk sectors like children’s media, political advertising, or branded content.

The standard of care is still emerging. But courts may start to treat autonomous AI systems as akin to employees or subcontractors, entities whose actions create exposure for the firms that deploy them.

• Product Liability

In some cases, plaintiffs may seek to apply product liability law, arguing that the AI system itself is a defective product. This is more likely if harm results from software bugs, design flaws, or inadequate warnings, such as an AI tool trained on copyrighted data that reliably outputs infringing material.

This approach could shift liability upstream, to model developers or Application Programming Interface (API) providers. But that remains a largely untested theory.

• Intellectual Property Infringement

Many AI systems train on copyrighted material and may output near-replicas of the same. Recent lawsuits have alleged that this constitutes unauthorized use or derivative creation.

If a brand uses an AI to generate a soundtrack for a commercial, and that soundtrack closely resembles a known copyrighted song, they may be liable for infringement, even if they had no idea it occurred.

• Obscenity and Incitement

If AI-generated content contains sexually explicit, violent, or hateful material, companies may be exposed to liability under state obscenity laws, FCC regulations, or incitement doctrines. Again, lack of human oversight does not automatically shield the publisher.

Insurance Implications: Coverage in Conflict

The rise of autonomous AI-generated content is creating a test for the insurance industry. Many existing coverage lines are not designed to address the unique attributes of these risks, especially when harm is unintentional or arises without human action.

Media E&O (Errors & Omissions)

Media liability policies are intended to protect against risks such as defamation, invasion of privacy, and intellectual property (IP) infringement. However, many policies were written before generative AI and may include:

• Exclusions for automated content
• Requirements for human editorial review
• Ambiguity around “authorship” and “intent”

If a company cannot demonstrate control over or awareness of the harmful content, claims may be denied.

Cyber Liability

Some companies assume AI-related risks are covered under cyber policies. Most cyber policies focus on data breaches, network security, and ransomware, not content produced by software. Coverage for reputational harm, third-party IP claims, or defamation may be limited or excluded.

General Liability

CGL policies generally exclude harm arising from professional services or media production. That leaves a gap when AI-generated content causes bodily injury (e.g. a virtual reality (VR) game triggers seizures) or mental anguish.

Emerging Solutions

Some carriers are beginning to adapt. Armilla, for example, offers AI-specific warranties and policies focused on model governance. Google Cloud includes embedded liability protections for certain AI services, under strict conditions. But few insurers have defined risk scoring methodologies for generative content.

Hypothetical Scenarios: What Could Go Wrong?

Consider these plausible and likely risk events:

• Defamatory Game Dialogue: A procedurally generated character in a role-playing game uses a racial slur or mocks a real-world political figure. A viral clip leads to lawsuits and brand boycotts
• Unauthorized Celebrity Deepfake: An AI-driven ad features a digitally generated spokesperson who resembles a famous actor. No licensing was obtained. The actor sues for violation of their right of publicity
• Misinformation in Political Ads: A campaign uses AI to generate regionalized attack ads, some of which contain false claims. The candidate is sued for defamation; the platform is investigated for failure to moderate
• Harmful Kids’ Content: An AI video generator creates a cartoon for children that contains disturbing imagery or language. Parents protest, regulators intervene, and advertisers flee

In each case, the absence of human oversight does not eliminate liability and may heighten it.

Regulatory Momentum: Lawmakers Are Responding

While federal legislation on AI remains limited, several U.S. states have introduced targeted laws to address AI-generated content:

• Tennessee’s ELVIS Act (2024) makes it illegal to use AI to replicate a person’s voice or likeness without consent, extending existing right-of-publicity protections to generative tools
• Florida’s Political Ad Disclosure Law (2023) mandates that AI-generated political content be clearly labeled, a sign of growing concern over election misinformation
• California’s proposed AI bills target deepfakes, child safety, and AI governance in digital media

At the federal level, agencies like the FTC, NTIA, and Copyright Office are actively exploring frameworks for oversight. The FTC, in particular, has signaled that deceptive or harmful AI use may trigger enforcement under Section 5 of the FTC Act (unfair or deceptive practices).

Expect further regulatory movement in the next couple of years, especially as high-profile incidents or litigation capture public attention.

Who’s on the Hook? Understanding the Risk Chain

Responsibility for autonomous AI content typically falls to the party with the greatest control. But in complex media ecosystems, this can include:

• Studios and Agencies: If they deploy AI without adequate governance or disclaimers, they may be liable for the content created and distributed
• Tech Providers: In some cases, AI vendors may be liable for model behavior, especially if they misrepresent its safety or misuse training data
• Distributors and Platforms: Platforms may be exposed if they monetize or fail to moderate harmful AI content. Section 230 protections are narrowing
• Advertisers and Sponsors: Brands whose products are associated with problematic content may face reputational or contractual fallout

Understanding and allocating risk contractually is critical and rare.

What Leaders Must Do Now

To manage the threat landscape, organizations must take steps across governance, legal, and insurance functions.

• Governance and Controls
  Establish internal policies on when and how autonomous AI can be used. Require human review for any public-facing content. Mandate model testing, documentation, and versioning.
• Contracts and Indemnity
  Negotiate clear indemnification clauses with AI vendors and content distributors. Ensure contracts specify who bears liability for content harms, IP infringement, or regulatory violations.
• Monitoring and Audit Trails
  Implement systems to detect offensive or infringing content before it reaches the public. Log and retain AI decision-making processes for defensibility.
• Insurance Strategy
  Work with brokers to audit current policies for gaps in coverage. Consider endorsements or new products focused on AI-specific liability. Push for clarity in underwriting language.
• Executive Oversight
  Ensure that AI use is included in enterprise risk management programs, board reporting, and D&O coverage discussions. Treat it as a tier-one operational risk.