10 Security Risks You Need To Know When Using AI For Work
In Brief
By mid-2025, AI is deeply embedded in workplace operations, but widespread use—especially through unsecured tools—has significantly increased cybersecurity risks, prompting urgent calls for better data governance, access controls, and AI-specific security policies.
By mid‑2025, artificial intelligence is no longer a futuristic concept in the workplace. It’s embedded in daily workflows across marketing, legal, engineering, customer support, HR, and more. AI models now assist with drafting documents, generating reports, coding, and even automating internal chat support. But as reliance on AI grows, so does the risk landscape.
A report by Cybersecurity Ventures projects global cybercrime costs to reach $10.5 trillion by 2025, reflecting a 38 % annual increase in AI-related breaches compared to the previous year. That same source estimates around 64 % of enterprise teams use generative AI in some capacity, while only 21 % of these organizations have formal data handling policies in place.
These numbers are not just industry buzz—they point to growing exposure at scale. With most teams still relying on public or free-tier AI tools, the need for AI security awareness is pressing.
Below are the 10 critical security risks that teams encounter when using AI at work. Each section explains the nature of the risk, how it operates, why it poses danger, and where it most commonly appears. These threats are already affecting real organizations in 2025.
Input Leakage Through Prompts
One of the most frequent security gaps begins at the first step: the prompt itself. Across marketing, HR, legal, and customer service departments, employees often paste sensitive documents, client emails, or internal code into AI tools to draft responses quickly. While this feels efficient, most platforms store at least some of this data on backend servers, where it may be logged, indexed, or used to improve models. According to a 2025 report by Varonis, 99% of companies admitted to sharing confidential or customer data with AI services without applying internal security controls..
When company data enters third-party platforms, it’s often exposed to retention policies and staff access many firms don’t fully control. Even “private” modes can store fragments for debugging. This raises legal risks—especially under GDPR, HIPAA, and similar laws. To reduce exposure, companies now use filters to remove sensitive data before sending it to AI tools and set clearer rules on what can be shared.
Hidden Data Storage in AI Logs
Many AI services keep detailed records of user prompts and outputs, even after the user deletes them. The 2025 Thales Data Threat Report noted that 45% of organizations experienced security incidents involving lingering data in AI logs.
This is especially critical in sectors like finance, law, and healthcare, where even a temporary record of names, account details, or medical histories can violate compliance agreements. Some companies assume removing data on the front end is enough; in reality, backend systems often store copies for days or weeks, especially when used for optimization or training.
Teams looking to avoid this pitfall are increasingly turning to enterprise plans with strict data retention agreements and implementing tools that confirm backend deletion, rather than relying on vague dashboard toggles that say “delete history.”
Model Drift Through Learning on Sensitive Data
Unlike traditional software, many AI platforms improve their responses by learning from user input. That means a prompt containing unique legal language, customer strategy, or proprietary code could affect future outputs given to unrelated users. The Stanford AI Index 2025 found a 56% year-over-year increase in reported cases where company-specific data inadvertently surfaced in outputs elsewhere.
In industries where the competitive edge depends on IP, even small leaks can damage revenue and reputation. Because learning happens automatically unless specifically disabled, many companies are now requiring local deployments or isolated models that do not retain user data or learn from sensitive inputs.
AI-Generated Phishing and Fraud
AI has made phishing attacks faster, more convincing, and much harder to detect. In 2025, DMARC reported a 4000% surge in AI-generated phishing campaigns, many of which used authentic internal language patterns harvested from leaked or public company data. According to Hoxhunt, voice-based deepfake scams rose by 15% this year, with average damages per attack nearing $4.88 million.
These attacks often mimic executive speech patterns and communication styles so precisely that traditional security training no longer stops them. To protect themselves, companies are expanding voice verification tools, enforcing secondary confirmation channels for high-risk approvals, and training staff to flag suspicious language, even when it looks polished and error-free.
Weak Control Over Private APIs
In the rush to deploy new tools, many teams connect AI models to systems like dashboards or CRMs using APIs with minimal protection. These integrations often miss key practices such as token rotation, rate limits, or user-specific permissions. If a token leaks—or is guessed—attackers can siphon off data or manipulate connected systems before anyone notices.
This risk is not theoretical. A recent Akamai study found that 84% of security experts reported an API security incident over the past year. And nearly half of organizations have seen data breaches because API tokens were exposed. In one case, researchers found over 18,000 exposed API secrets in public repositories.
Because these API bridges run quietly in the background, companies often spot breaches only after odd behavior in analytics or customer records. To stop this, leading firms are tightening controls by enforcing short token lifespans, running regular penetration tests on AI-connected endpoints, and keeping detailed audit logs of all API activity.
Shadow AI Adoption in Teams
By 2025, unsanctioned AI use—known as “Shadow AI”—has become widespread. A Zluri study found that 80% of enterprise AI usage happens through tools not approved by IT departments.
Employees often turn to downloadable browser extensions, low-code generators, or public AI chatbots to meet immediate needs. These tools may send internal data to unverified servers, lack encryption, or collect usage logs hidden from the organization. Without visibility into what data is shared, companies cannot enforce compliance or maintain control.
To combat this, many firms now deploy internal monitoring solutions that flag unknown services. They also maintain curated lists of approved AI tools and require employees to engage only via sanctioned channels that accompany secure environments.
Prompt Injection and Manipulated Templates
Prompt injection occurs when someone embeds harmful instructions into shared prompt templates or external inputs—hidden within legitimate text. For example, a prompt designed to “summarize the latest client email” might be altered to extract entire thread histories or reveal confidential content unintentionally. The OWASP 2025 GenAI Security Top 10 lists prompt injection as a leading vulnerability, warning that user-supplied inputs—especially when combined with external data—can easily override system instructions and bypass safeguards.
Organizations that rely on internal prompt libraries without proper oversight risk cascading problems: unwanted data exposure, misleading outputs, or corrupted workflows. This issue often arises in knowledge-management systems and automated customer or legal responses built on prompt templates. To combat the threat, experts recommend applying a layered governance process: centrally vet all prompt templates before deployment, sanitize external inputs where possible, and test prompts within isolated environments to ensure no hidden instructions slip through.
Compliance Issues From Unverified Outputs
Generative AI often delivers polished text—yet these outputs may be incomplete, inaccurate, or even non-compliant with regulations. This is especially dangerous in finance, legal, or healthcare sectors, where minor errors or misleading language can lead to fines or liability.
According to ISACA’s 2025 survey, 83% of businesses report generative AI in daily use, but only 31% have formal internal AI policies. Alarmingly, 64% of professionals expressed serious concern about misuse—yet just 18% of organizations invest in protection measures like deepfake detection or compliance reviews.
Because AI models don’t understand legal nuance, many companies now mandate human compliance or legal review of any AI-generated content before public use. That step ensures claims meet regulatory standards and avoid misleading clients or users.
Third-Party Plugin Risks
Many AI platforms offer third-party plugins that connect to email, calendars, databases, and other systems. These plugins often lack rigorous security reviews, and a 2025 Check Point Research AI Security Report found that 1 in every 80 AI prompts carried a high risk of leaking sensitive data—some of that risk originates from plugin-assisted interactions. Check Point also warns that unauthorized AI tools and misconfigured integrations are among the top emerging threats to enterprise data integrity.
When installed without review, plugins can access your prompt inputs, outputs, and associated credentials. They may send that information to external servers outside corporate oversight, sometimes without encryption or proper access logging.
Several firms now require plugin vetting before deployment, only allow whitelisted plugins, and monitor data transfers linked to active AI integrations to ensure no data leaves controlled environments.
Lack of Access Governance in AI Tools
Many organizations rely on shared AI accounts without user-specific permissions, making it impossible to track who submitted which prompts or accessed which outputs. A 2025 Varonis report analyzing 1,000 cloud environments found that 98 % of companies had unverified or unauthorized AI apps in use, and 88 % maintained ghost users with lingering access to sensitive systems (source). These findings highlight that nearly all firms face governance gaps that can lead to untraceable data leaks.
When individual access isn’t tracked, internal data misuse—whether accidental or malicious—often goes unnoticed for extended periods. Shared credentials blur responsibility and complicate incident response when breaches occur. To address this, companies are shifting to AI platforms that enforce granular permissions, prompt-level activity logs, and user attribution. This level of control makes it possible to detect unusual behavior, revoke inactive or unauthorized access promptly, and trace any data activity back to a specific individual.
What to Do Now
Look at how your teams actually use AI every day. Map out which tools handle private data and see who can access them. Set clear rules for what can be shared with AI systems and build a simple checklist: rotate API tokens, remove unused plugins, and confirm that any tool storing data has real deletion options. Most breaches happen because companies assume “someone else is watching.” In reality, security starts with the small steps you take today.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
