1. Ignoring Prompt Injection Attacks
Prompt injection has become the number one security risk for applications using large language models (LLMs). This attack manipulates the AI by embedding malicious instructions within seemingly harmless inputs. Unlike traditional hacks that exploit code,
prompt injection targets the model's reasoning layer. For example, an attacker could hide a command in a document or support ticket that the AI is summarizing, tricking it into emailing sensitive data to an external address. Because the AI cannot reliably distinguish its core instructions from user-provided text, it follows the malicious command. To mitigate this, enterprises must treat all inputs as untrusted and implement firewalls and monitoring that can detect and block instructions that violate security policies.
2. Allowing Sensitive Data in Public AI Tools
The fastest way to leak proprietary data is for an employee to paste it into a public AI chatbot. When teams use free or consumer-grade AI tools to summarize notes, write code, or draft emails, that sensitive information—financial data, client lists, source code—leaves the company's secure environment. That data can be stored on third-party servers and may even be used to train future versions of the public model. This mistake rarely happens with malicious intent; it's a byproduct of employees trying to be more productive. The solution is not to ban AI, but to provide sanctioned, enterprise-grade AI tools and establish clear policies that educate employees on what is and isn't safe to share.
3. Neglecting Data and Model Poisoning
An AI model is only as reliable as the data it's trained on. Data poisoning occurs when attackers deliberately corrupt that training data to introduce biases, backdoors, or critical failures. This manipulation can cause the model to produce dangerously incorrect outputs or create hidden triggers that an attacker can exploit later. This risk is especially high when using external data sources or pre-trained models from third-party repositories. Defending against this requires a focus on data provenance—knowing where your data comes from—along with rigorous integrity checks, access controls for data pipelines, and continuous monitoring to detect anomalous model behavior.
4. Applying an Outdated Security Playbook
Many organizations make the mistake of treating AI security as a simple extension of their existing IT security framework. This approach fails because AI systems introduce entirely new types of vulnerabilities. Traditional security focuses on networks, code, and infrastructure, whereas AI security must also govern the model's behavior, data inputs, and outputs. A common failure is focusing on governance as a paper exercise—drafting policies without implementing technical controls. If underlying issues like overly broad data permissions exist, AI will exploit and amplify them. A modern AI security strategy requires a new playbook that accounts for the entire AI lifecycle, from data sourcing to model deployment and monitoring.
5. Granting Over-Permissive Access
When an AI tool is integrated into enterprise systems, it inherits the access rights of the user or service account running it. A critical mistake is giving AI models and agents overly broad permissions to access internal databases, file shares, and applications. This creates a massive blast radius if the AI is compromised. An attacker who successfully manipulates an over-privileged AI through prompt injection could potentially access or exfiltrate data from every system the AI can reach. The principle of 'least privilege' is paramount. AI systems should only be granted the absolute minimum access required to perform their specific function. Regular audits of these permissions are essential to ensure they haven't become too permissive over time.
6. Failing to Monitor and Vet the Supply Chain
Very few companies build their own foundational models from scratch. Most rely on a complex supply chain of third-party models, APIs, and open-source packages. Each of these components represents a potential security risk. A vulnerability or backdoor in a pre-trained model or a dependency can be inherited by every application that uses it. Despite this, many organizations deploy these external AI assets without thorough security vetting or ongoing monitoring. A robust AI security program must include strict vendor assessments, the use of trusted and verified AI development packages, and continuous monitoring of all AI components in production to watch for signs of compromise or unexpected behavior.
