Analysis and practical guides on AI security, AI-enabled attacks, data leakage in generative AI tools, prompt injection, shadow AI, deepfakes, AI governance and emerging technology risks. We help companies use AI faster while keeping security and compliance risks under control.
How to Use ChatGPT Safely in a Company?
Using ChatGPT safely in a company requires clear rules: use business accounts instead of personal ones, do not paste confidential data into unapproved tools, define prohibited data, manage access, control apps and plugins, enable MFA, train employees and verify AI outputs before business use. ChatGPT can significantly increase productivity, but only when the company has policy, controls and risk awareness.
Prepared by: CCyber Editorial Team
To use ChatGPT safely in a company, the organization should implement an AI usage policy, use a business workspace instead of personal accounts, define prohibited data, enable MFA, control user access, manage apps and plugins, monitor usage, train employees and require verification of outputs before business use. The main risks are confidential data leakage, uncontrolled Shadow AI, incorrect answers, personal data use, unapproved apps and automated actions performed without proper controls.
July 2026
ChatGPT can significantly accelerate knowledge work. Employees use it for writing, analysis, summarization, translation, document preparation, ideation, data organization, coding, offer creation, project planning and learning new topics.
Typical company use cases:
The problem is not that employees want to use ChatGPT. The problem appears when they do it without rules, without company awareness, without data controls and without understanding AI limitations.
An employee may paste customer data, contracts, personal data, source code, technical documentation, sales plans, financial data, NDA-covered data or other confidential information into ChatGPT.
The risk increases if the employee uses a personal account, unapproved tool or does not understand where the data goes and which rules apply to the specific plan.
Shadow AI means using AI tools without company knowledge or approval. This may include a personal ChatGPT account, external application, plugin, transcription tool, AI agent or informal automation process.
Shadow AI is dangerous because the company does not know:
ChatGPT may produce answers that sound convincing but are incomplete, outdated, incorrect or not aligned with the company’s context. This is especially important for law, tax, regulation, security, code, medicine, finance and business decisions.
Practical rule: ChatGPT can help prepare an analysis, draft or list of questions, but a responsible person in the company must verify the result.
Pasting personal data into an AI tool may create legal and organizational risks. This includes data about customers, employees, candidates, patients, users, suppliers and contact persons.
The company should clearly define when personal data may be used in ChatGPT, in which plan, on what basis, for what purpose and with what safeguards.
ChatGPT can connect with apps and data sources. This increases capabilities, but also increases risk. An app may read data, use conversation context and in some cases perform actions in connected systems.
The biggest risks:
Prompt injection occurs when external text, a file, website, email or document contains hidden instructions attempting to influence the model’s behavior. The risk is higher when ChatGPT analyzes internet content, third-party files or data from connected apps.
Companies should be especially careful in scenarios where ChatGPT has access to confidential data and can also send information to external services or perform actions.
A personal account may be convenient, but it is not a good model for working with company data. The company does not control access, retention, sharing, history, privacy settings, app connections, employee offboarding or usage rules.
A personal account may be acceptable only for public or neutral data, if company policy allows it.
A business workspace such as ChatGPT Business or ChatGPT Enterprise allows better management of access, users, settings, apps, roles, retention, security and compliance. This is the right direction if the company wants to use ChatGPT regularly at work.
The company should consider a business workspace if:
The simplest and most important part of an AI policy is the prohibited data list. Employees must know what must not be entered into ChatGPT without company approval.
Practical employee rule: before pasting data into ChatGPT, ask: could I send this information to an external provider without extra approval?
Without an owner, the topic will be scattered across IT, legal, HR, compliance, sales and business teams. The company should clearly define who owns the rules for using ChatGPT.
The owner should coordinate:
Evidence to prepare: AI Security owner decision and responsibility scope.
The company should decide whether employees may use personal accounts or only a business workspace. For work with company data, a controlled business environment is the better direction.
Check:
Evidence to prepare: decision on the approved ChatGPT plan and personal account rules.
The policy should not be long. It should be understandable for employees and address the most common situations.
The policy should define:
Evidence to prepare: company policy for safe ChatGPT use.
Employees must know what they can use ChatGPT for. If the company only prohibits, people will find workarounds. Clear practical examples work better.
Examples of allowed use:
Examples requiring approval:
Evidence to prepare: list of approved and approval-required use cases.
Access to company ChatGPT should be managed like access to other important tools. The company should know who has access, what permission level they have and when access is removed.
Minimum baseline:
Evidence to prepare: user and admin list with latest access review.
Apps and plugins can connect ChatGPT with external services, search data, use context and perform actions. This area requires special control.
Before enabling an app, check:
Safest starting model:
Evidence to prepare: register of approved apps, plugins, actions and connections.
Employees may create their own GPTs, projects, upload files and share materials. This is convenient, but may lead to uncontrolled sharing of company knowledge.
Define rules:
Evidence to prepare: rules for GPTs, projects and file use.
ChatGPT can be very helpful, but it should not be the only source of decisions. The company should clearly define which outputs require verification.
Verification is especially required for:
Practical rule: ChatGPT can help prepare a draft, but a responsible human approves the final version.
Evidence to prepare: AI output verification rules by task type.
Training should show real situations from company work, not only general rules. Employees should be able to distinguish safe use from risky use.
Training should cover:
Evidence to prepare: training materials, participant list and knowledge test.
The company should measure not only the number of users, but also risks, use cases, incidents, policy violations and business value.
Monitor:
Evidence to prepare: quarterly AI Security board report.
If the company does not provide clear rules, employees often start using personal accounts. This creates Shadow AI and lack of data control.
A total ban often fails. Employees will still use AI if they see real value. A safer environment, clear rules and approved use cases work better.
Employees must know whether they can paste a contract, customer data, code, CV or invoice. Without a clear list, decisions will be random.
Connecting ChatGPT to company tools without control may provide access to too much data or allow actions the company did not approve.
ChatGPT may be wrong. Using outputs without verification in legal, finance, code, compliance or customer communication may create business risk.
The company should know what to do if an employee pasted confidential data, connected an unapproved app or used an incorrect answer in an important process.
Safe use of ChatGPT affects IT, security, legal, HR, compliance, sales, the board and employees. It is an organizational topic, not only a technical one.
A consulting company has 60 employees. Employees use ChatGPT for writing emails, summarizing documents, creating offers, analyzing notes and preparing sales materials. The board assumes usage is low, but a quick survey shows that more than half of the team uses personal accounts.
A review shows that fragments of customer contracts, CRM data, meeting notes and NDA-covered content have been pasted into ChatGPT. There was no AI policy, prohibited data list, approved workspace, training or output verification rules.
The company implements a controlled model in 90 days. First, it creates a policy and prohibited data list. Then it launches a business workspace, MFA, training, use case register and app approval process. After 90 days, the board receives the first report: who uses ChatGPT, for what, what the risks are and what must happen next.
The result is not reduced innovation. Employees still use ChatGPT, but they do it more safely, with lower data leakage risk and higher business value.
ccyber.io helps companies implement ChatGPT and other AI tools in a safe, practical and business-risk-aligned way. We focus on policy, data, users, apps, training, Shadow AI, incidents and control evidence.
We can support your organization with:
The best first step is a short ChatGPT Security Assessment. It helps check how the company already uses ChatGPT, which data is at risk, which controls are needed and how to move quickly from uncontrolled use to a safe operating model.
Yes, but it requires rules, controls and training. The safest model is a business workspace, clear prohibited data list, MFA, access control, app management and AI output verification.
For company data, a company account or business workspace is a better model. A personal account may be allowed only for public or neutral data if company policy permits it.
Do not paste passwords, MFA codes, tokens, API keys, customer data, personal data, confidential contracts, source code without approval, financial data, NDA-covered data or strategic information.
Yes. Outputs may be incorrect, incomplete or not aligned with context. Results must be verified, especially in law, finance, compliance, cybersecurity, code and customer communication.
They can be risky if they access company data or perform actions in connected systems. The company should approve apps, limit write actions and control connections.
Activate the AI incident procedure: determine data scope, account, plan, conversation, files, recipients, apps, possible legal impact and remediation actions. Fast reporting without hiding the mistake is important.
It can support analysis, but only under company policy and after data assessment. Contracts often contain confidential information, personal data and NDA-covered data, so using them in ChatGPT requires an approved environment and legal review.
Start with current usage inventory, a decision on a business workspace, prohibited data list, short policy, MFA, employee training and a register of approved use cases.
ChatGPT can be a very valuable business tool, but it should not be deployed chaotically. The biggest risk is not AI itself, but lack of rules: personal accounts, confidential data pasted without control, unapproved apps, no output verification and no incident process.
Safe ChatGPT use requires a simple model: business workspace, AI policy, prohibited data list, MFA, user control, app management, training, output verification and risk reporting to the board.
The best companies do not block AI without an alternative. They give employees a safe way to work, clear rules and practical examples. This allows ChatGPT to increase productivity without creating uncontrolled risk for company data, customers and reputation.
Shadow AI: The Hidden Risk of AI Tools in Organizations
Shadow AI means the use of AI tools in an organization without the company’s knowledge, approval or control. It may include personal AI accounts, unapproved apps, plugins, transcription tools, coding assistants and AI agents. The biggest risks are data leakage, confidentiality breaches, lack of vendor control, wrong decisions, legal issues and missing compliance evidence.
Prepared by: CCyber Editorial Team
Shadow AI occurs when employees use AI tools without the organization’s knowledge, approval or control. This usually happens when the company does not provide safe tools, clear rules and a simple approval process. Shadow AI may lead to leakage of customer data, source code, confidential documents, personal data, company secrets and information covered by contracts. The best response is not a ban alone, but visibility, an AI use policy, data classification, approved tools, training, monitoring and an AI incident response process.
July 2026
Shadow AI means using AI tools in an organization outside official company control. It may involve personal accounts, free tools, browser extensions, mobile applications, transcription tools, coding assistants, presentation generators, chatbots or AI agents without approval from IT, security, compliance or legal teams.
Shadow AI is similar to Shadow IT, but the risk can be higher. With Shadow IT, an employee may store files in an unapproved application. With Shadow AI, an employee may enter data into a system that analyzes, processes, summarizes, stores it in chat history, uses it in integrations or passes it further through plugins and external connections.
Examples of Shadow AI:
Shadow AI appears because AI tools genuinely help employees work faster. Employees do not always want to bypass rules. They often want to write an email faster, prepare an offer, summarize a meeting, translate a document, analyze data or generate code.
Common causes:
In practice, Shadow AI is a signal to the board: people want to use AI, but the organization has not yet created a safe and convenient operating model.
Employees may paste customer requests, contracts, correspondence, call recordings, project data, requirements, complaints or implementation documents into AI.
AI tools may receive data about employees, candidates, customers, patients, users or contractors. This may create personal data protection risk.
Developers may send code fragments, logs, errors, configurations, secrets, API keys, database queries or diagnostic data to AI.
AI is often used to summarize strategy, sales plans, board documents, financial information, product roadmaps or business secrets.
Customer data, technical documentation, contract terms, project information and materials covered by NDAs should not go into tools without checking the legal basis, contract and processing rules.
The most obvious risk is entering information into an AI tool that the company should not share with an external provider. This may include a contract, customer data, code, strategy document, financial information or technical secrets.
The problem is that the company may not know that such a data transfer happened. If the tool is used through a personal account, outside company control, it is hard to determine data scope, activity history and accountability.
If an employee enters personal data into an unapproved AI tool, the company may not know who the provider is, where data is processed, how long it is stored, whether it is used to improve the service and whether the right data processing agreement exists.
In technical teams, Shadow AI may lead to leakage of source code, API keys, tokens, configuration files, logs, database schemas and system architecture information.
This risk is especially important for software houses, SaaS companies and digital product manufacturers because code and architecture are part of business value and product security.
An AI tool may look like a simple application, but behind it there is a vendor, models, infrastructure, subcontractors, data storage, terms of use and security posture. Without vendor assessment, the company does not know whether the tool meets security, privacy and compliance requirements.
AI may generate answers that are convincing but incorrect. If an employee uses output without verification, the company may make a wrong legal, financial, technical, HR, sales or operational decision.
Shadow AI can lead to employees treating AI output as truth. This is dangerous when analyzing contracts, vulnerabilities, code, financial data, HR decisions, customer communications and risk assessments.
Shadow AI is not only public chatbots. More SaaS applications include AI features by default. A business team may enable such a feature without IT knowing, and the tool may analyze documents, conversations, support tickets, CRM data or customer history.
An AI agent may take actions, not only return text. If it has access to email, files, CRM, project systems, code repositories or calendars, its permissions should be controlled like the permissions of a technical user.
An unsafe AI agent may perform unauthorized actions, send data, download files, change records or act based on manipulated instructions.
The company may have a security policy, but if it does not know which AI tools are used, it has no control evidence. This makes audits, customer discussions, compliance and incident response more difficult.
Prompt injection is a technique for manipulating a language model application through specially crafted instructions. It may occur when AI processes user content, documents, websites, emails, files or data from external systems.
In the Shadow AI context, the problem is larger because the company may not know:
Unapproved AI tools should therefore be treated not only as a privacy risk, but also as a cybersecurity risk.
The simplest response is banning AI use. In practice, a ban alone often fails. Employees will continue to use tools if they see real value and the company does not provide a safe alternative.
A better approach:
A ban without an alternative creates hiding. Good governance creates visibility.
Shadow AI affects IT, security, legal, data protection, HR, procurement, finance and business teams. Without an owner, the topic will be fragmented.
The owner should coordinate:
Evidence to prepare: AI Security owner decision, responsibility scope and supporting team list.
You cannot control what you cannot see. The first practical step is determining which AI tools employees use and for what tasks.
Include:
Evidence to prepare: AI tool register, business owner, data type, purpose of use and approval status.
Not every AI use has the same risk. Improving the style of an email is very different from analyzing customer data, source code, CVs, medical documentation or contracts.
Example classification:
Evidence to prepare: AI use case register with risk rating.
Employees need to know what must not be entered into unapproved AI tools.
The prohibited list should include:
Evidence to prepare: data classification rules and prohibited data list for unapproved AI tools.
The company should create a list of AI tools allowed for work. Employees should know which tool they can use for which task.
The approved tool list should show:
Evidence to prepare: approved AI tool list and usage rules.
An AI vendor should be assessed like any important technology provider. The company should know where data is processed, how long it is stored, who can access it and which contractual terms apply.
Check:
Evidence to prepare: AI vendor assessment, approval decision and usage terms.
AI tools should be managed like other company systems. Access should be granted, reviewed and removed when an employee leaves.
Minimum baseline:
Evidence to prepare: AI tool user list, roles, MFA configuration and latest access review.
The company should monitor whether confidential data is sent to unapproved AI tools. This is not about reading every employee conversation, but about visibility into data movement and responding to policy violations.
Possible controls:
Evidence to prepare: AI monitoring report, violation list and corrective actions.
AI agents need separate rules because they can perform actions in systems. They should not have more permissions than necessary.
Control:
Evidence to prepare: AI agent register, permission scope, action logs and human approval points.
Shadow AI training should be practical. Employees need examples from their work, not generic slides about artificial intelligence.
Training should cover:
Evidence to prepare: training materials, attendee list, knowledge test and confirmation of policy acceptance.
The company should know what to do if an employee pasted confidential data into an unapproved tool, an AI agent performed an unwanted action or an AI tool disclosed data.
The process should define:
Evidence to prepare: AI incident procedure and AI incident or policy violation register.
Shadow AI should be visible to the board because it affects data, compliance, reputation, vendors and work efficiency. The report should not be technical. It should show risk and decisions.
The report may include:
Evidence to prepare: quarterly AI Security board report.
If employees need AI for work, a ban alone usually moves use to personal accounts and unapproved tools. A better approach combines rules, approved tools and monitoring.
Employees often do not know whether they can paste a contract, CV, customer data, code fragment or internal document into AI. Without a clear list, they will decide on their own.
Shadow AI affects legal, data protection, HR, procurement, sales, customer support and the board. IT alone cannot solve the problem without business support.
A company may block public chatbots while still using AI features in CRM, HR, marketing, customer support and document systems. These features also require assessment.
An AI agent with system access should have limited permissions, logs, approval points and an owner. It should not operate like an invisible unmanaged user.
If the approval process is unclear or too slow, employees will use tools without approval. The process should be simple, fast and understandable.
The company may have good intentions, but without a tool register, vendor assessments, training, logs and reports, it is difficult to show customers or auditors that AI is under control.
A services company has 180 employees. Employees use AI to write offers, translate emails, summarize meetings, analyze contracts and create marketing content. The board believes the company does not officially use AI because it has not bought any enterprise AI tool.
A short assessment shows that employees are using more than a dozen AI tools through personal accounts. Contract fragments, customer data, meeting notes, candidate CVs, code and proposal documents have been entered into these tools. There is no tool list, no vendor assessment, no AI policy and no training.
The problem is not that employees want to use AI. The problem is that the company has not provided them with a safe operating model.
Within 90 days, the company creates an approved tool list, prohibits confidential data in unapproved tools, assesses vendors, enables company login, trains employees and creates a process for requesting new AI tools.
After implementation, the company does not block innovation. Employees still use AI, but in a more controlled way, aligned with risk and explainable to customers and auditors.
ccyber.io helps companies identify and reduce Shadow AI without blocking sensible use of artificial intelligence. We focus on visibility, data, vendors, employees, AI agents, processes and control evidence.
We can support your organization with:
The best first step is a short Shadow AI assessment. It helps determine which AI tools are already used, what data is at risk, which departments need support and which actions should come first.
Shadow AI is the use of AI tools in an organization without the company’s knowledge, approval or control. It may include personal AI accounts, public chatbots, browser extensions, transcription tools, coding assistants and AI features in SaaS applications.
Not every AI use is bad. The problem is lack of control over the tool, data, vendor, permissions and outputs. The goal should not be blocking AI, but managing AI use safely.
The highest-risk data includes customer data, personal data, financial data, confidential documents, business secrets, non-public financial information, restricted source code, passwords, API keys and technical secrets.
Usually not. A ban alone may move AI use to personal accounts and hidden tools. A better approach is approved tools, clear rules, training, monitoring and a simple approval process.
No. Shadow AI may include many tools: chatbots, content generators, meeting tools, coding assistants, AI features in SaaS, plugins, browser extensions and AI agents.
The best approach combines surveys, business team interviews, SaaS application analysis, traffic monitoring to AI tools, procurement review, browser extension control and employee reporting.
Shadow AI is uncontrolled AI use. AI governance is a system of rules, roles, processes, risk assessments, approvals and monitoring that enables controlled AI use.
The best starting point is an AI tool inventory, AI use policy, prohibited data list, approved safe tools, vendor assessment and employee training.
Shadow AI is one of the most important new organizational risks because AI entered daily work faster than policies, training and security controls.
The biggest problem is not artificial intelligence itself. The biggest problem is lack of visibility: the company does not know which tools employees use, what data goes into them, who has access and what vendor terms apply.
The best strategy combines security and usability: approved tools, clear rules, data classification, vendor assessment, access control, monitoring, training and board reporting. This allows the company to use AI faster, but without uncontrolled risk.
AI Security for Companies: Where Should You Start?
AI Security does not start with banning artificial intelligence. It starts with an inventory of AI tools, acceptable use rules, data classification, access control, vendor assessment, data leakage prevention, training and monitoring risks such as shadow AI, prompt injection, deepfakes and overreliance on AI outputs.
Prepared by: CCyber Editorial Team
Companies should start AI Security with a simple and practical programme: identify which AI tools employees use, what data is entered into them, which processes depend on AI, who approves tools and what controls are required. The first priorities are an acceptable AI use policy, data classification, access control, vendor assessment, employee training, shadow AI monitoring, LLM application security and an AI incident response plan.
July 2026
AI Security means securing systems, processes and data related to artificial intelligence. It includes secure use of ready-made AI tools as well as security of applications, models, integrations, training data, prompts, outputs, AI agents and automation.
In practice, AI Security answers the following questions:
AI Security is not about blocking innovation. The goal is to help the company use AI faster, but without uncontrolled risk.
Artificial intelligence has entered companies faster than many other technologies. Employees use it to write emails, analyze documents, summarize meetings, generate code, prepare offers, translate content, analyze data and support customers.
The problem is that many organizations do not know:
This creates shadow AI risk, meaning the use of AI tools outside the knowledge and control of the organization. Shadow AI is especially risky when employees paste customer data, personal data, source code, company secrets, contractual documents or financial information into public tools.
You cannot secure what you cannot see. The first step is an inventory of AI tools used officially and unofficially.
The inventory should include:
Evidence to prepare: AI tools register, business owner, vendor, data type, purpose of use and approval status.
The tool name alone is not enough. What matters more is how the company uses AI. The risk level depends on the use case.
Example use cases:
Not every use case has the same risk level. AI used to improve the style of an email has a different risk profile than AI used for candidate selection, customer assessment, financial data access or automated action in business systems.
Evidence to prepare: list of AI use cases with risk rating: low, medium, high.
The biggest starting issue is data. Employees often do not know whether they can paste a contract, customer data, CV, source code, financial report or strategy document into AI.
The company should clearly define what must not be pasted into public or unapproved AI tools.
Commonly prohibited data should include:
Evidence to prepare: data classification and rules for using data in AI tools.
An AI policy does not need to be 50 pages long. At the beginning, it should be short, clear and understandable for employees.
A good policy should answer:
The policy should be supported by training. A document alone is not enough if employees do not understand examples from their daily work.
Evidence to prepare: AI use policy, employee acceptance confirmation and training materials.
AI often operates as an external service. This means data, prompts, files, outputs and metadata may be sent to a vendor. The company should understand how the vendor protects data and what contractual terms apply.
When assessing an AI vendor, check:
Evidence to prepare: AI vendor assessment, accepted contractual terms, DPA and tool approval decision.
AI tools should be managed like other business systems. Access should be controlled, monitored and removed when an employee leaves.
Minimum access controls:
If an AI tool has access to email, files, CRM, code repositories or financial systems, it should be treated as a high-risk system.
Evidence to prepare: AI user list, SSO/MFA configuration, roles, administrators and latest access review.
If the company builds its own chatbot, LLM application, AI agent or model API integration, the risks are higher than when using a ready-made tool.
Consider at least:
An LLM application should be treated as high risk if it has access to sensitive data, users, payments, internal systems or can automatically take actions.
Evidence to prepare: AI application threat model, data list, integration list, permission controls, prompt injection tests and activity logs.
In many processes, AI should support humans, not replace decisions without control. This is especially important for decisions about customers, employees, finances, security and legal matters.
Human-in-the-loop means that a human:
The biggest mistake is treating AI output as automatically true. AI can produce convincing but incorrect answers.
Evidence to prepare: decision process description, human role, approval points and AI output verification rules.
AI Security does not end with policy and training. The company should monitor whether tools are used according to rules and whether incidents occur.
Monitor:
For AI applications, it is useful to log prompts, outputs, decisions, tools used and actions performed by the AI agent, while respecting privacy and data minimization principles.
Evidence to prepare: AI incident register, usage logs, alerts and periodic AI risk report.
The best AI policy will not work if employees do not understand how AI can harm the company. Training should be practical and example-based.
Training should cover:
Evidence to prepare: training attendance list, materials, knowledge test and refresher schedule.
Shadow AI is the use of AI tools outside the knowledge and control of the organization. It can involve public chatbots, browser extensions, mobile apps, transcription tools, presentation generators or coding plugins.
The risk is that the company does not know what data is sent to the tool, where it is processed and who can access it.
An employee may paste customer data, a contract, personal data, code, a strategy document or financial information into AI. Even if done with good intentions, the company may lose control of the information.
Prompt injection is an attempt to manipulate an LLM application through specially crafted instructions. It can lead to data disclosure, rule bypass, undesired action or ignoring system instructions.
An AI agent that can take actions in systems creates higher risk than a chatbot that only returns text. If the agent has access to email, files, CRM, internet, financial systems or code repositories, its permissions must be limited.
AI makes it easier to create fake images, voices and video. This increases the risk of executive impersonation, urgent payment fraud, bank account change fraud and employee manipulation.
AI can generate convincing errors. Overreliance on AI outputs can lead to poor business decisions, legal mistakes, code defects, incorrect data analysis and wrong customer communication.
An AI vendor may change terms, model behavior, processing location, data usage or security posture. The company should monitor critical AI vendors like other technology suppliers.
Employees should know which AI tools are allowed for work. If they want to use a new tool, there should be a simple approval path.
Personal data, customer data, company secrets, passwords, API keys, restricted code and confidential documents should not be pasted into public or unapproved AI tools.
AI output should be treated as a suggestion, not as automatic truth. Before using an output in a decision, offer, code, analysis or customer communication, it should be verified.
If an AI tool has access to email, files, CRM, code repositories or production systems, its permissions should be limited to the minimum necessary.
Employees should know where to report situations where they pasted the wrong data into AI, a tool behaved suspiciously or AI output caused an error.
A complete AI ban often leads to shadow AI. Employees will still look for tools that help them work faster. A better approach is clear rules, approved tools and training.
A company cannot manage risk if it does not know where AI is used. Inventory is the foundation of every AI Security programme.
The key question is not only “which tool do we use?”. The more important question is: what data goes into AI and what impact does AI output have on business decisions?
AI can be wrong, and incorrect answers often look convincing. Human verification is required in business processes.
An AI agent with access to systems can take real actions. Without limits, logs and approvals, it can become a new source of operational risk.
AI vendors process data and may become critical to business processes. They should be assessed for security, privacy, contracts, data retention and incident handling.
The AI Act introduces a risk-based approach and distinguishes between prohibited AI systems, high-risk systems, transparency risks and minimal-risk systems. For companies in the EU, this means assessing whether a specific AI use case may be subject to regulatory requirements.
ISO/IEC 42001 concerns AI management systems. It is useful for organizations that want to structure policies, roles, processes, risks, documentation and continual improvement in AI.
NIST AI RMF helps manage AI risk through the Govern, Map, Measure and Manage functions. It is a practical way to translate AI from broad principles into a risk management process.
OWASP LLM Top 10 helps technical teams understand key LLM application risks, including prompt injection, insecure output handling, data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, excessive agency and overreliance.
A service company has 120 employees. Employees use public AI tools to write proposals, analyze contracts, translate documents and generate marketing content. IT does not have a full list of tools, and the board assumes the topic is not urgent because the company does not build its own AI model.
A short assessment shows that employees have pasted customer data, contract fragments, confidential documents and source code into AI tools. Some tools operate as browser extensions, some have access to email and some accounts were created with private email addresses.
The problem is not the use of AI itself. The problem is the lack of rules, risk ownership, data classification, approved tools and access control.
Within 30 days, the company can reduce risk: create an AI tool register, prohibit pasting confidential data into public tools, approve secure enterprise versions, train employees and introduce a process for approving new tools.
ccyber.io helps companies use AI safely, practically and without blocking innovation. We focus on real risks: data, employees, vendors, LLM applications, governance and control evidence.
We can support your organization with:
The best first step is a short AI Security workshop. It helps quickly determine where the company already uses AI, what data is at risk, which tools need approval and which actions should be implemented first.
Usually not. A total ban often leads to shadow AI. A better solution is to define approved tools, prohibit confidential data in unapproved systems and train employees.
The best starting point is an inventory of AI tools, use cases, data classification, acceptable AI use policy and employee training.
Shadow AI is the use of AI tools outside the knowledge and control of the organization. It can include public chatbots, browser extensions, SaaS apps and AI tools used through private accounts.
Passwords, API keys, personal data, customer data, confidential documents, company secrets, non-public financial data and restricted source code should not be pasted into public or unapproved AI tools.
Prompt injection is a technique that manipulates an LLM application through specially crafted instructions. It can lead to data disclosure, rule bypass, ignored system instructions or unwanted actions.
No. AI Security also applies to companies using ready-made AI tools, chatbots, copilots, SaaS applications with AI features or model integrations through APIs.
Not every AI use has the same level of obligations. The AI Act follows a risk-based approach. A company should check whether its AI use is high-risk, requires transparency or falls under specific rules.
At least quarterly and whenever a new tool, use case, vendor, system integration or data processing method is introduced.
AI Security for companies should not start with panic or bans. It should start with visibility, rules and controls. The company must know where AI is used, what data goes into tools, who approves use and what risks are accepted.
The most important first actions are AI tool inventory, acceptable use policy, data classification, employee training, vendor assessment and LLM application security.
Organizations that implement AI Security early will be able to use AI faster, more safely and with greater trust from customers, employees and the board.
We write about regulations such as NIS2, DORA, CRA and ISO 27001, cybersecurity for SMEs and critical infrastructure, as well as cyber hygiene, emerging threats and building security awareness within organisations.
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