On September 14th, 2026, AI frontier lab OpenAI called on UK lawmakers to impose regulations to tackle the security threats posed by the rapid development of artificial intelligence. This follows an unsolicited attack from an OpenAI agent that broke the firewalls of startup Hugging Face earlier this year.
Cybersecurity isn’t a new issue for business leaders, but its significance is increasing in the age of AI. Clients trust them with information, employees don’t want to disclose their salaries or investors don’t want to make their investments public.
Without clear governance and processes on the use of AI, these are all at risk of being leaked. Doing nothing poses an existential risk for firms.
A new class of security risks
AI presents great opportunities to speed up repetitive tasks, like data reporting, leaving more time for employees to produce more strategic, quality work for clients. For an agent to become useful, it needs as much context and information access as possible, but this is where there is the greatest potential for something to go wrong if not managed correctly.
For instance, agents may have access to bookkeeping, reporting, document management and CRM systems, meaning a successful attack could expose highly sensitive client information.
The main issue is LLMs have difficulty distinguishing data from instructions. An agent can interpret instructions from an email, a document or a webpage, even if those are malicious. These are called prompt injection attacks. Basically hackers manipulate generative AI systems by feeding them with malicious inputs disguised as legitimate user prompts.
Another threat is data leaking. An agent can visit a website or perform a web search and potentially release confidential information, for example by incorporating company data into a URL or a search query. This could expose sensitive data to the Internet without the company or the agent noticing.
The risk becomes particularly acute when three things come together: an agent receives untrusted inputs, accesses sensitive information and takes action in external systems – the so-called Lethal Trifecta. A malicious instruction hidden in an email, document or webpage can potentially trigger actions at a later point using the agent's legitimate permissions.
Therefore, firms need to set strict guardrails around each agent to limit what data it can access, what capabilities it has and what actions it is allowed to take. This is essential to avoid information leaks or an AI agent going rogue.
The threat sitting inside a company
AI adoption is scaling across all levels of a business, from finance to marketing, sales to product development. New research from Turbotic shows 51% of business leaders fear employees are feeding sensitive company data into unauthorized AI tools and 36% are concerned about employees using AI without oversight. This is called Shadow AI.
When an employee prompts a public AI platform, it leaks sensitive client information and receives output that looks confident but could be inaccurate. This could lead to inconsistent tone, misaligned advice or data breaches. The problem isn’t the technology itself, but it’s the lack of traceability and human review. Placing too much confidence in AI tools without the right guardrails is dangerous.
Most of the time, employees don’t even realize the security issues that pertain to AI usage.
They need to understand that experimentation outside any approved workflow or data safeguard is a security risk. Sharing sensitive information with a public model or using content generated by AI without reviewing it risks greater exposure to compliance breaches, AI slop and reputational damage.
Most importantly, employees become responsible for output they cannot explain, verify or trace.
To remove this risk, leaders need to democratize AI by giving employees access and clear guidance on the tools they’re allowed to use, how to provide the right context to AI agents, set up processes to review AI output and establish any guardrails to prevent data breaches. This approach that we call Organizational AI will make a firm more capable from security and capacity perspectives.
Managing the risks of AI
Despite increasing warnings about AI security threats, fewer than 1% of organizations have fully operationalized responsible AI, according to a report from the World Economic Forum and Accenture.
Given the risks associated with free AI usage, having a responsible AI policy is now obligatory.
Without restrictions on basic access controls, employees may be able to use AI to surface information they shouldn’t have access to, from colleagues’ salaries or confidential investor data. Once an AI agent is connected to an organization's data, weak permissions are a liability and it can more easily surface accidental access.
Anything that relates to company data, client communications, or that influences professional advice needs to be controlled by clear governance. It’s the responsibility of leaders to provide employees with auditable and transparent boundaries, and step one involves providing clear guidelines on what, when and how AI tools can be used. Then comes explainability combined with governance and oversight to generate trust.
The critical need for Organizational AI
Firms need to get the fundamentals right: What data can AI access? Where does the input come from? What systems can it connect to? What actions is it allowed to take? When must a human review or approve the output? All of these should be clearly communicated to employees and baked into processes.
Organizational AI is implementing a system that runs across the whole firm that the organization can grow into for a better client experience, team development and security management.
Companies should start now. The existential risk of doing nothing is bigger than the risk of doing it badly.
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