The risk of a malicious deepfake video targeting an executive is high enough that it requires a formal protocol in your crisis communications plan. This plan should detail contacts at social platforms and outline the immediate response to mitigate reputational damage.
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A viral thread showed a user tricking a United Airlines AI bot using prompt injection to bypass its programming. This highlights a new brand vulnerability where organized groups could coordinate attacks to disable or manipulate a company's customer-facing AI, turning a cost-saving tool into a PR crisis.
For AI agents, the key vulnerability parallel to LLM hallucinations is impersonation. Malicious agents could pose as legitimate entities to take unauthorized actions, like infiltrating banking systems. This represents a critical, emerging security vector that security teams must anticipate.
Beyond data privacy, a key ethical responsibility for marketers using AI is ensuring content integrity. This means using platforms that provide a verifiable trail for every asset, check for originality, and offer AI-assisted verification for factual accuracy. This protects the brand, ensures content is original, and builds customer trust.
Navigating technological upheaval requires the same crisis management skills as operating in a conflict zone: rapid pivoting, complex scenario planning, and aligning stakeholders (like donors or investors) around a new, high-risk strategy. The core challenges are surprisingly similar.
Treating AI risk management as a final step before launch leads to failure and loss of customer trust. Instead, it must be an integrated, continuous process throughout the entire AI development pipeline, from conception to deployment and iteration, to be effective.
Organizations must urgently develop policies for AI agents, which take action on a user's behalf. This is not a future problem. Agents are already being integrated into common business tools like ChatGPT, Microsoft Copilot, and Salesforce, creating new risks that existing generative AI policies do not cover.
If your brand isn't a cited, authoritative source for AI, you lose control of your narrative. AI models might generate incorrect information ('hallucinations') about your business, and a single error can be scaled across millions of queries, creating a massive reputational problem.
Duolingo CEO's internal memo prioritizing AI over hiring sparked a public backlash. The company then paused its popular social media to cool down, which directly led to a slowdown in daily active user growth. This shows how internal corporate communications, when leaked, can directly damage external consumer-facing metrics.
Insurers like AIG are seeking to exclude liabilities from AI use, such as deepfake scams or chatbot errors, from standard corporate policies. This forces businesses to either purchase expensive, capped add-ons or assume a significant new category of uninsurable risk.
During a crisis, a simple, emotionally resonant narrative (e.g., "colluding with hedge funds") will always be more memorable and spread faster than a complex, technical explanation (e.g., "clearinghouse collateral requirements"). This highlights the profound asymmetry in crisis communications and narrative warfare.
