AI as an insider threat: threat points and mitigation strategies

Explore the different security risks associated with using AI technologies in a business context and the various mitigation strategies that businesses can use to protect themselves, their network and their valuable data.

Since the release of ChatGPT in late 2022, the world has witnessed a tremendous shift in the use of AI technology by both organizations and individuals. In the business world, companies rushed to adopt the new technology in an effort to increase innovation, cut costs and improve different business processes. But as with any new technology, this rapid adoption comes with a price. In the cyber world, that price is security.

Major threats of AI in business

While AI technologies provide a robust solution for some businesses to increase growth and innovation, using AI for business operations may pose significant security and privacy challenges.

Data privacy issues

AI solutions are trained on massive volumes of data, parts of this data could be taken from company internal communications, such as interactions with customers or from other work-related tasks. Part of the trained data contains sensitive customer and employee information; if it falls into the wrong hands, it will cause a data breach.

On the other hand, employees may also be using AI tools to make their work a bit easier. For example, some are turning to AI chatbots to help summarize long reports or even generate snippets of code. But here is the thing—when employees provide sensitive information like financial records or proprietary data into these systems, that info might get stored, processed or even used to train future AI models. So, if a marketing team member uploads customer lists to analyze them, there is a chance they are compromising personal customer data without realizing it or the implications of who they are sharing it with. Similarly, financial analyst summarizing quarterly reports might accidentally expose revenue figures to external systems.

Cybersecurity risks

Malicious actors are increasingly using AI tools like ChatGPT and Claude to pull off more convincing and personalized phishing emails. There are even phishing-specific services now like, FraudGPT. These tools help them develop messages that look legitimate to an organization and employee alike, and can slip past spam filters, making scams way sneakier and harder to spot.

Deepfake technology, which works by mimicking a person's voice and appearance, has advanced rapidly, driven by AI development. Deepfake allows hackers to impersonate executives during video calls, such as to authorize fraudulent wire transfers, or create fake audio recordings of CEOs requesting employees to share sensitive information or bypass common security protocols.

We should also consider the ability of generative AI to produce programming code. The massive capability of AI allows hackers to create malware that evades detection systems by generating polymorphic code that changes its signature, or by creating programming scripts to automate vulnerability scanning across corporate networks.

Risks of AI cloud-based solutions

Many AI solutions are built using cloud-based platforms. While this reduces costs and allows businesses to scale up and down easily, it also introduces various entry points for threat actors to exploit. Companies lose direct control over their data processing and become dependent on third-party security measures. On the other hand, a breach in the cloud provider's infrastructure could expose organizations sensitive data to risks. Inadequate access controls may allow unauthorized personnel to access sensitive business models or training data.

Bias and discrimination

AI solutions depend heavily on the machine learning (ML) models used to train them. If there is bias in the data used to train the model, this bias will also reflect in the AI solution and its results. For example, in hiring processes, AI screening tools may systematically exclude qualified candidates from certain demographics or geographical regions. In financial services, loan approval algorithms might unfairly deny credit to specific communities. Customer service chatbots may provide different levels of assistance based on perceived customer characteristics, which could potentially violate anti-discrimination laws and damage brand reputation.

Lack of transparency

Most AI solutions operate using the “black box” model, where the AI system does not give the reasons or explanations of why it produces a specific result or decision. This raises questions over AI solution fairness, accountability and trust.

Copyright infringement

When AI chatbots generate content, it brings up questions about who owns the rights and who is liable if something goes wrong. These bots learn from various sources — such as books, magazines, websites and social media posts. But most of them do not tell you where their info comes from. This is especially concerning if they are pulling data from copyrighted stuff like books or articles without getting permission from the original creators first. This can cause real problems for businesses. For example, marketing teams might create campaign materials using AI, not realizing they are accidentally copying copyrighted text, images or ideas. That could lead to lawsuits from the original creators. Or, if developers use AI to write code, they might end up including protected algorithms or proprietary functions without permission, risking violations of licensing agreements and potential IP claims.

Over-reliance on AI

Relying heavily on AI for security can open the door to new risks. If organizations do not have humans overseeing the systems, they might miss important threats, and biases in AI models can cause blind spots. 
Plus, they can start feeling overly confident in tech which is not perfect, making them more vulnerable to cyberattacks. For example, AI-driven threat detection might overlook certain attack patterns that hackers learn to mimic. When security teams just trust these AI tools without double-checking, attackers can study how AI systems work and create attacks that slip right past them. Say an AI keeps marking certain network traffic as safe — hackers could pretend to be that traffic to surpass detection.

Automated incident responses can be tricked, too. Attackers might flood the system with false alarms, causing the AI solution to get overwhelmed or ignore real threats. This could lead to a scenario where a major breach happens while the system is busy sorting through a mountain of minor alerts.

How to use AI solutions safely at work?

Organizations must implement comprehensive strategies to harness AI benefits while minimizing their security risks. The following lines provide practical frameworks for safe AI adoption in business environments.

Establish clear usage policies

Organizations should develop explicit guidelines outlining the acceptable AI use cases and prohibited activities. For instance, these policies must specify which types of data employees can input into AI systems and which information that should remain strictly confidential. For example, financial records, customer personally identifiable information (PII), and proprietary information should never be processed through external AI solutions. Marketing teams may use AI for content ideation, but should not upload customer databases, while development teams may seek coding assistance without sharing proprietary source code with the AI-powered chatbots.

Establish data classification system

Businesses must categorize their information assets based on sensitivity levels and establish the corresponding AI usage restrictions. Without clear classification, employees may inadvertently expose critical data to AI tools, which can finally result in a data leak.

• Public information (Unrestricted AI use) – This includes all non-sensitive information that can be freely disclosed to the public, such as marketing content, public financial reports and press releases.
• Confidential information (Controlled AI use) – This data is sensitive, but exposing it to external parties may not be catastrophic for the business. Using this data in AI systems should be controlled and require approval (e.g., on-premise AI, private LLMs or enterprise-grade tools with strict data controls). For example, a bank may feed customer transactions to an internal AI solution to analyze customer usage patterns for fraud detection. However, such data should not be shared with external parties.
• Highly sensitive information (AI prohibited) – Such data can cause catastrophic damage if leaked to external parties, such as customer PII and financial records, in addition to trade secrets. Such data should be handled manually and never entered into AI systems.

Deploy internal AI solutions

To mitigate risks when using AI solutions — especially for sensitive operations — businesses should prioritize internal AI deployments (on-premises or private cloud) over public AI tools. Internal AI systems provide greater control over data processing, storage and access while at the same time reducing exposure to third-party security risks. In addition to this, organizations can customize these solutions to meet specific business requirements and compliance obligations.

For instance, when using public AI tools such as ChatGPT and Gemini, they pose risks to organizations via:
 

• Data leaks – employees may post proprietary code into ChatGPT to get programming advice
• No control over training data (AI providers may keep user inputs)
• Regulatory non-compliance (sharing PII with external parties is not allowed in some regulatory bodies, like HIPAA)

Deploying an internal AI solution allows organizations to mitigate these risks by:

• Keeping data within the organization's IT environment
• Enforce strict access control mechanisms to protect AI solutions and data
• Complying with industry-specific regulations

On the other hand, private AI setups let organizations train their models using their own sensitive data without worrying about it getting leaked or accessed externally. It is a great way to stay innovative, keep your competitive edge, while remaining in compliance with enforced regulatory frameworks.

Conduct regular security assessments

As businesses are increasingly relying on AI solutions to perform critical tasks, conducting a regular security assessment ensures these systems remain immune to AI-specific vulnerabilities that can lead to data breaches. 

For instance, AI systems introduce unique security risks such as:

• Data leaks (AI models may memorize sensitive user inputs)
• Adversarial attacks (threat actors can manipulate AI outputs)
• Third-party risks (cloud AI vendors may mishandle data, or their infrastructure may introduce misconfigured points that can lead to data exposure)

To mitigate these risks, proactive security testing prevents such incidents.

Assess AI vendor before implementation

Before adopting an AI solution, businesses should consider the following areas:

• Understand the data handling policies of the vendor – For example, where AI data is stored and whether user queries are used to train the model?
• Does the AI vendor carry compliance certifications such as GDPR, HIPAA, SOC 2 or ISO 27001.
• Has the vendor undergone a regular security audit? (e.g., executing penetration testing against its infrastructure by an independent auditor)

For example, if we have a healthcare institution that wants to deploy an AI solution while still complying with HIPAA. The approved vendor should use end-to-end encryption, allow on-premises deployment, and have HIPAA compliance certification.

AI adoption in business environments requires a balanced approach that maximizes innovation while minimizing security exposure. To exploit AI capability safely, organizations should implement comprehensive usage policies, maintain human oversight when using AI solutions, and prioritize data classification. The key lies in treating AI as a powerful tool that demands careful management rather than a replacement for good security practices. Businesses that proactively address these challenges will gain competitive advantages while protecting their critical assets from emerging threats.

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