The common AI security risks your company faces
And how to mitigate them
Published on: June 23, 2025
Last update: June 23, 2025
When Tony Stark, a brilliant engineer, built the Iron Man suit in the movie Iron Man (2008), he intended to use it to save lives. His groundbreaking technology was quickly hijacked by bad actors who tried to use it against him and innocent people. His technology, which was meant for good, became a tool for ill in the hands of criminals.
As dramatic as that sounds, today’s adoption of AI by businesses around the world is following a similar script. While AI offers incredible benefits for companies, it also presents new security risks that many organizations are not yet fully prepared to address. And unlike in the movies, there’s no superhero to swoop in and save you.
In this article, I explore the common AI risks companies face and share practical steps to help business leaders secure their AI journey before attackers turn this innovation into their next weapon.
The double-edged sword of AI: a risky innovation
There’s no doubt that AI helps companies be more efficient, from improving developer experience to accelerating product development. However, this impressive technology can also be used by bad actors to launch more sophisticated attacks. Some common attacks include:Â
- Attackers now use genAI to create emails and messages that look real, tricking employees into sharing sensitive information.Â
- Fraudsters can clone a CEO’s voice or image to authorize fraudulent transactions or mislead stakeholders.
- Hackers can tamper with AI models, especially open source ones, making them contradict the intended outcomes of the AI models.
Why businesses should be concerned about AI risks
Today, companies are adopting AI without fully understanding the security implications. In regions where regulatory frameworks are constantly evolving, businesses are more vulnerable to supply chain risks, data privacy violations, and AI model theft.
In 2024, global AI adoption ramped up to 72% and is now at 78%. This marks a significant increase from previous years. However, only 26% of companies have developed the necessary capabilities to move beyond pilot projects and achieve real value from AI implementations.
Ignoring AI security risks could lead to financial losses, reputational damage, and severe regulatory penalties.
Top AI security risks to watch out for
While security risks are always evolving, the following is a list of common challenges that every business and technology leader needs to be aware of.
1. Data leakage through poor AI governance
When businesses integrate AI without stringent governance, they risk exposing sensitive customer or internal data. Many genAI platforms can unintentionally memorize and output sensitive information from training or inference data. This risk is amplified when using third-party LLMs with unclear data retention or usage policies.
Additionally, the system instructions used to control the behavior of the AI model could contain sensitive information. This sensitive information risks getting leaked if the system prompt is leaked.
Mitigation:
- Define strict data usage policies, especially when dealing with sensitive datasets. Avoid feeding personal or confidential information into third-party models.
- Encrypt or mask system prompts and restrict access to them.Â
- Adopt zero trust principles and continuously audit model outputs to ensure no leakage occurs.
2. Dependency on third-party AI models and components
Third-party or open source models can introduce security flaws, such as supply chain vulnerabilities. Poorly maintained or malicious models may contain backdoors, biases, or outdated libraries that become weak links in your application.Â
Moreover, they may not have adequate protection against model theft (in case of internally built models), which could lead to intellectual property loss.
Mitigation:
- Thoroughly vet any third-party model before integration.Â
- Use models from reputable sources that provide transparency into their training processes and update cycles.Â
- Keep track of all model dependencies with a Software Bill of Materials (SBOM) or, in this case, Machine Learning Bill of Materials (ML-BOM), and monitor them for new vulnerabilities or suspicious updates.Â
3. Database manipulation in RAG systems
If your AI system uses Retrieval Augmented Generation (RAG), where an LLM pulls information from external sources using vector search, then you need to be aware of the hidden risks in your embeddings. Attackers can exploit weaknesses in how vectors are created, stored, or retrieved to inject misleading content, skew search results, or even access private documents that shouldn’t be exposed.
For example, someone could intentionally add toxic or deceptive content to your vector database or manipulate embeddings to surface sensitive internal data through cleverly crafted queries.
Mitigation:
- Sanitize and validate all content before it is added to your vector database.
- Implement access controls and monitoring on your vector store.
- Regularly audit embedding integrity and scan for anomalies that may indicate poisoning or manipulation attempts.
4. AI hallucinations
One of the most dangerous risks in LLM-based systems is misinformation. This occurs when the AI produces content that may seem accurate, but is factually wrong. This usually happens due to AI hallucination, where the model fills in knowledge gaps using patterns from its training data instead of verified facts.
In enterprise settings, this can be catastrophic. A hallucinated policy clause, legal citation, or health recommendation might mislead customers, expose your organization to lawsuits, or erode public trust. Worse still, users may unknowingly rely on these outputs without cross-checking them. This problem is known as overreliance.
Mitigation:
- Always cross-check critical outputs against trusted knowledge sources or human reviews.Â
- Use RAG to ground AI responses in real data.Â
- Clearly label AI-generated content and avoid using it in isolation for high-impact decisions.
Today, companies are adopting AI without fully understanding the security implications. In regions where regulatory frameworks are constantly evolving, businesses are more vulnerable to supply chain risks, data privacy violations, and AI model theft.
5. Unchecked AI outputs
When the output of a language model is used directly—whether on a website, inside a business system, or passed into another service without first checking or cleaning it—you’re opening the door to serious risks.Â
Attackers can craft prompts that make the model generate malicious content. A few examples include cross-site scripting (XSS), cross-site request forgery (CSRF), and server-side request forgery (SSRF), which trick users into performing unwanted actions that can harm the business
Mitigation:
- Never trust LLM output by default.Â
- Sanitize all outputs before use, and apply proper encoding for their context, whether it's HTML, JavaScript, or SQL.
- Use content security policies and rate-limiting to further reduce exposure.
6. AI bias and regulatory risks
When AI models are trained on biased or incomplete data, they may produce discriminatory outputs, violating compliance mandates like the EU AI Act or UK Equality Act. This poses a great risk where overreliance on models for critical decisions (for example, hiring, lending, medical diagnosis) results in unfair or unethical outcomes.
Mitigation:
- Conduct regular fairness and bias audits.Â
- Ensure that your training data is diverse and representative.Â
- Introduce human-in-the-loop workflows for sensitive decisions and stay updated with the latest legal and ethical guidelines.
7. AI input and response manipulation
LLMs can be tricked by attackers into executing harmful commands or revealing internal logic using crafted prompts. This trickery, known as prompt injection, allows attackers to override system instructions or inject malicious payloads, particularly in chatbots, email assistants, or RAG-powered systems.
Mitigation:
- Isolate user input from system prompts.
- Use prompt segmentation and strict input validation.Â
- Apply filters to detect suspicious language patterns and continuously test your system for injection vulnerabilities.
8. Model denial of service
Attackers may flood a model with complex or large token inputs to exhaust system resources. This can cause service outages or ramp up compute costs, especially in API-driven LLM integrations.
Mitigation:
- Implement request throttling and input size limits.Â
- Monitor usage patterns for spikes or anomalies.
- Use caching where possible to reduce repeated expensive queries.
9. Over-autonomous AI agents
LLM systems with plugins or agent capabilities are often granted too much autonomy, such as calling APIs or triggering backend functions without proper checks. If an LLM hallucinates a response, misinterprets a prompt, or is influenced by a prompt injection, it could take damaging actions like altering data, sending sensitive emails, or executing business logic incorrectly.Â
This risk escalates in multi-agent setups where one compromised or poorly performing agent can mislead others.Â
Mitigation:
- Restrict the scope of what AI agents are allowed to do.Â
- Always require human approval for high-risk actions.Â
- Apply least privilege principles and maintain audit logs of all automated agent activities.
10. Lack of monitoring and abuse detection
AI systems can be deployed without robust logging or abuse detection. Without visibility into how your model is being used or abused, threat detection becomes nearly impossible.
Mitigation:
- Set up real-time monitoring for model interactions.Â
- Track usage by endpoint, user, and behavior patterns.
- Implement alerting for suspicious activity and keep detailed logs for forensic analysis.
When AI models are trained on biased or incomplete data, they may produce discriminatory outputs, violating compliance mandates like the EU AI Act or UK Equality Act.
Get ahead of AI security risks
AI is here to stay, but so are the risks. As a business leader, the question is not whether you should adopt AI, but how you can do it securely. Don’t wait for an attack to happen before you act. You may not be Iron Man, but staying on top of risks can be your superpower. Start by reviewing your AI security strategy today.Â
