Narrative Agility: How GenAI Enables Rapid Adaptation by Threat Actors
Much of the recent discussion on GenAI and harmful activity focuses on scale - a quantitative increase in the capacity to generate new content. Researchers, civil society organizations, and platforms document how GenAI lowers the cost of content creation, accelerates translation, and enables threat actors to produce content at volumes previously unattainable. [1]
But scale alone does not capture the full scope of risk. The more consequential shift is not the quantity of content, but how quickly narratives can adapt and evolve in response to audience feedback, platform defenses, and external events.
GenAI enables a distinct capability—narrative agility—that qualitatively changes how threat actors adapt and evolve in response to their environment.
From Capacity to Capability
For the purposes of this analysis, narrative agility refers to the ability of an actor to rapidly test, adapt, and redeploy narratives in response to audience feedback, platform defenses, and external events. This includes assessing engagement signals, iterating messaging based on performance (e.g. engagement and virality), and pivoting narratives as conditions change.
Any threat actor whose objectives depend on manipulation, recruitment, or indoctrination can benefit from rapid feedback and adaptation loops. Specific examples include terrorist organizations refining recruitment narratives, financially motivated scammers optimizing social-engineering scripts, and nihilistic online communities coercing self-harm.
None of these behaviors are new. What GenAI changes is the speed, frequency, and ease with which feedback can be analyzed and incorporated into messaging. What was once labor-intensive, time-consuming, and episodic becomes low-friction, continuous, and potentially automated. Narrative development shifts from discrete campaigns to an ongoing, responsive process.
Narrative Agility as a Second-Order Risk
Like other second-order risks, narrative agility does not require the model to generate prohibited content. The prompts that enable it are often non-violative, such as requests for sentiment analysis, audience targeting, or messaging pivots. From a Trust & Safety or Intelligence perspective, this distinction matters. Existing safeguards focus on violative prompts and outputs, leaving narrative agility largely unmitigated.
This risk is particularly difficult to address because it combines benign interactions with rapid adaptation. Platforms can recognize and disrupt established patterns of abuse. However, a capability that evolves in response to enforcement actions, audience feedback, and external events quickly degrades the value of known signals and current interventions. As threat actors adjust content, targeting, and tactics in near-real time, initially successful detection and prevention measures may prove temporary.
Conclusion
The risks GenAI introduces are not limited to the volume of content it enables. More consequential is how it reshapes the processes that enable harmful activity.
Narrative agility illustrates this challenge. Platform defenders must both identify harmful content and contend with threat actors whose messaging continuously changes in response to the environment. The problem expands beyond blocking outputs to understanding and interrupting the processes that allow influence, recruitment, or exploitation efforts to evolve faster than traditional mitigation approaches.
Endnotes
Erin Saltman & Skip Gilmour, “Artificial Intelligence: Threats, Opportunities, and Policy Frameworks for Countering VNSAs,” https://gifct.org/wp-content/uploads/2025/04/GIFCT-25WG-0425-AI_Report-Web-1.1.pdf, pg. 5.