Our blog features practical articles on cybersecurity and regulations, written in plain language that business owners and managers can understand. We cover specific topics such as NIS2, DORA and CRA requirements, ISO 27001 implementations, incident analysis, phishing, ransomware and best practices for SMEs.
Instead of technical jargon we offer actionable guidance that can be applied to your business straight away. We focus on content you can implement immediately: checklists, threat analyses, SME guides and regular updates on new regulations.
Is Voice Cloning Voice Phishing? AI Threats and Risks Explained Simply
Voice cloning and voice phishing are not the same, but they often appear together. Voice cloning is a technique for creating a synthetic voice similar to a real person. Voice phishing is a scam conducted by phone, voice message or messenger to steal money, data, passwords, MFA codes or access. AI can make such an attack sound like a real CEO, employee, customer, supplier or family member.
Prepared by: CCyber Editorial Team
Voice cloning is not the same as voice phishing, but it can be a tool used in voice phishing. Voice cloning means creating a synthetic voice similar to a real person. Voice phishing, also known as vishing, means deceiving a person through a phone call, voice message or messenger. The goal may be a payment, MFA code, password reset, bank account change, system access or data disclosure. The most important rule for non-technical users is: if someone calls with an urgent request for money, data, password, access or secrecy, do not judge only by voice. Break the pressure and confirm the request through another known channel.
July 2026
Voice cloning is the use of AI technology to create a synthetic voice that sounds similar to a real person. Such a voice may imitate tone, speaking pace, accent, emotion and characteristic speech patterns.
To create a voice sample, an attacker may use publicly available material, for example:
Voice cloning itself may have legitimate uses, for example in digital accessibility, voiceovers, content localization or tools supporting people who have lost their voice. The problem begins when someone uses this technology without consent or for fraud.
Voice phishing, often called vishing, is a scam conducted through voice. It may be a phone call, voice message, messenger call, app call or audio recording. The goal is to persuade a person to take an action they normally would not take.
The attacker may try to steal:
Voice phishing can work without AI. A fraudster may simply call and pretend to be a bank, supplier, family member, IT employee or CEO. AI makes the attack more convincing because the voice may sound like someone you know.
No. These are two different concepts.
A technology for creating a synthetic voice similar to a real person.
A fraud method where the attacker uses a conversation or voice message to manipulate the victim.
An attacker may use voice cloning to conduct voice phishing. Example: a finance employee receives a call from someone who sounds like the CEO. The voice says that an urgent payment must be made and nobody should be told because the matter is confidential. Technically, this is voice phishing, and voice cloning is the tool that increases attack credibility.
Simply put:
Voice cloning is “how the fraudster sounds”. Voice phishing is “why the fraudster is calling”.
The attacker chooses a person whose voice may create trust or pressure. This may be a CEO, CFO, manager, customer, supplier, IT employee or family member.
The attacker searches for online recordings or obtains a short voice sample from another source. The more public speeches, podcasts and videos exist, the easier it is to prepare convincing material.
The fraudster prepares a story. It is usually urgent, stressful and unusual. Example: payment, bank account change, outage, confidential transaction, arrest, customer problem or urgent access reset.
Contact may happen by phone, voice message, messenger or as part of a larger attack that starts with email.
The attacker uses emotion and authority. They may say: “this is urgent”, “do not tell anyone”, “the customer is waiting”, “the board approved this”, “this is confidential”, “do it now”.
The victim makes a payment, provides an MFA code, changes supplier data, sends a document, resets a password or grants access.
The fraudster may ask for secrecy, move to another channel or request deletion of messages. Sometimes, after the first success, they try to force additional actions.
A finance employee receives a call from a voice that sounds like the CEO. The request concerns an urgent payment, often to a new recipient or foreign account.
The attacker calls after an earlier email about changing supplier bank details. The voice is supposed to confirm that the change is genuine.
The fraudster pretends to be an IT employee and asks for an MFA code, approval of a push notification or password reset.
A person sounding like a customer asks to send a contract, report, invoice, project data or documentation to a new email address.
The attacker impersonates a supplier and asks to change bank account number, contact email or portal login data.
A manager’s voice asks IT to quickly grant access to an employee, consultant or supplier.
The attack may combine voice and image. A meeting participant looks and sounds credible, but asks for an unusual decision, payment or process change.
A person receives a call from the voice of a loved one. The story involves an accident, arrest, hospital, debt or immediate financial need.
After the voice of the loved one, another person joins and increases pressure, saying money must be paid quickly.
The fraudster impersonates a bank and asks for a code, login data, app installation or “security transfer”.
A voice message or call sounds like a friend. The request involves a quick loan, payment or gift card purchase.
People naturally trust familiar voices. When you hear the speech pattern of someone you know, your brain quickly fills in the rest of the story.
Fear, shame, pressure, secrecy and authority reduce logical thinking. The fraudster wants the victim to act before checking facts.
A familiar number or contact name may appear on the screen. This does not always mean the call is genuine, because a number may be spoofed or communication may come through another account.
In the past, phishing was often exposed by language mistakes, strange accent or artificiality. Today, a scam may be fluent, correct and context-aware.
Do not trust voice alone.
If the request concerns money, data, password, MFA code, access, documents, bank account change or secrecy, always confirm it through a second channel.
A second channel means:
Do not use the phone number or link given by the caller in the suspicious conversation. Use a channel you already knew.
“You must do this now”, “we do not have time”, “the customer is waiting”, “the payment must go out today”.
“Do not tell anyone”, “this is confidential”, “the board does not want confusion”.
The CEO writes from a private messenger, a customer calls from a new number, a supplier asks to communicate outside the normal path.
“We will do the paperwork later”, “skip approval this time”, “this is an exception”.
Every urgent change of payment details should be treated as high risk.
Nobody should ask for an MFA code by phone. Such a request should not be fulfilled.
If someone asks to send documents to a new address, it must be independently verified.
If the caller insists that you must not call back, ask anyone or check, that is a strong warning sign.
Every unusual request for money, data, access, password, MFA code, bank account change or procedure bypass should be confirmed through another channel.
Large payments, new payment recipients, bank account changes, administrator access and sensitive data transfers should require approval by two people.
Do not call back the number provided in the conversation. Use a number from the contract, CRM, company directory or previously confirmed channel.
An MFA code, push notification and password reset link are security controls. Do not share them by phone, messenger or email.
For high-risk situations, the company can define a safety phrase. It should not be based on information available on social media.
It is not always possible to avoid recordings, but public appearances of high-risk people such as board members, finance, PR and decision makers should be managed consciously.
Training should show real situations: call from the “CEO”, payment request, supplier bank account change, MFA code, fake customer and deepfake in a meeting.
An employee should know where to report a suspicious call, voice message or meeting. Reporting should be simple and non-punitive.
If the call creates fear and urgency, that may be exactly what the fraudster wants.
Hang up and call the person who supposedly contacted you using the number already saved in your contacts.
If you cannot reach the person, call another family member or friend.
The fraudster often asks for quick transfer, cryptocurrency, gift cards or money transfer. That is a major red flag.
A family can agree on a short emergency phrase. It should not be public information or something easy to guess.
Do not share codes, passwords, bank data, document numbers or login details by phone.
This is not a good security control. The voice may sound familiar, and emotions may reduce caution.
If the suspicious request came by phone, continuing the same conversation is not verification.
If the company has no procedure, the employee decides alone, under pressure and often without evidence.
If employees are afraid to interrupt a call or ask a manager, the fraudster has an easier task.
Questions about family, school, city or pets may be available online. A safety phrase should not rely on such information.
The board’s voice may be used in an attack. The board should clearly communicate that procedures matter more than apparent urgency.
A suspicious call may be the first sign of a larger campaign against the company.
An employee may know the procedure in theory, but under stress will act the way they practiced. That is why exercises matter.
A finance employee receives a call from someone who sounds like the CFO. The caller says they are in a meeting with an investor and need an immediate transfer to a new supplier. They ask not to involve anyone else because the matter is confidential.
The employee knows the procedure. They do not make the transfer during the call. They end the call, check the CFO’s number in the company directory and contact them through a known channel. It turns out the CFO did not call and did not request the payment.
The company reports the incident to security, blocks the attempted supplier data change, informs the finance team and runs a short exercise for employees. The key outcome: the employee did not need to recognize whether the voice was synthetic. It was enough to follow the procedure.
ccyber.io helps companies prepare employees and processes for voice cloning, voice phishing, deepfakes and AI-supported fraud. We do not only teach technology recognition. We help build procedures that work even when the voice sounds credible.
We can support your organization with:
The best first step is a Voice Phishing and AI Scam Readiness Workshop. In a short workshop, the company can identify the most exposed processes, which procedures must change, who should be trained first and how to prepare simple protection against voice scams.
No. Voice cloning is a technology for creating a synthetic voice. Voice phishing is a voice-based scam. Voice cloning can be used for voice phishing.
Vishing is voice phishing. The attacker uses a phone call, voice message or messenger to steal money, data, password, MFA code or access.
Sometimes strange elements may be noticed, but this should not be relied on. Second-channel verification is safer.
Do not perform an unusual request during the call. End the contact and confirm through a known channel. If money, data or access are involved, use two-person approval.
No. The number may mislead, and contact may come from a compromised account or spoofing. Independent verification matters more.
A safety phrase can help, but it should not replace procedure. It works best with a second channel, two-person approval and exception documentation.
Start with finance, accounting, IT, HR, executive assistants, sales, customer service and people who can approve payments, access or data transfers.
Introduce second-channel verification, ban sharing MFA codes, use two-person approval for payments, prepare bank account change procedure and create a simple channel for reporting suspicious calls.
Voice cloning is a technology, while voice phishing is a scam. The combination is dangerous because the voice may sound like someone you trust, and the conversation may involve urgent money, data, access or password requests.
The best protection is not that an employee recognizes a synthetic voice. The best protection is that they know what to do: stop, do not act under pressure, confirm through a second channel, use two-person approval and report suspicious contact.
In the AI era, voice is no longer sufficient proof of identity. Companies that understand this early will reduce the risk of fake payments, data leaks, account takeover and wrong decisions under pressure.
Shadow AI: How Employees Use AI Outside Organizational Control
Shadow AI happens when employees use AI tools without the organization’s knowledge, approval or control. They often do it to write emails faster, analyze documents, create presentations, summarize meetings, write code or support customers. The problem is not AI itself, but the lack of rules, tool inventory, data controls, supplier assessment, logs, training and a safe alternative for employees.
Prepared by: CCyber Editorial Team
Shadow AI means using AI tools outside organizational control. An employee may use a private ChatGPT, Copilot, Gemini, Claude, Perplexity account, transcription tool, presentation generator, coding assistant or AI browser plug-in without the knowledge of IT, security, legal, compliance or their manager. The biggest risk appears when customer data, contracts, financial documents, HR data, source code, vulnerability information, strategic plans or trade secrets enter such a tool. The answer is not only a ban. The company should understand why employees use AI, prepare approved tools, clear policy, data classification, process for requesting new tools, training, risk-proportionate monitoring and an AI incident response procedure.
July 2026
Shadow AI is the use of artificial intelligence tools without formal approval, knowledge or supervision from the organization. It may be a public tool, private user account, free app, browser plug-in, code editor extension, meeting transcription tool, messaging bot or SaaS application with AI features purchased by a department without IT and security review.
Shadow AI may look harmless. An employee only wants to write an email faster, shorten a report, translate a document, prepare a meeting agenda, improve a proposal or summarize a customer call. The problem appears when data enters a tool that the company did not intend to share with an external provider.
Shadow AI includes, among other things:
Most Shadow AI cases do not result from bad intent. Employees use AI because they see immediate value: saving time, easier writing, faster analysis, less routine work and higher productivity.
This is an important lesson for the board: Shadow AI often reveals a real business need. The company should organize that need, not only punish its existence.
Employees paste correspondence, case descriptions, offer drafts, customer documents, complaints, notes and contracts into AI to prepare a reply or summary.
AI transcription and note tools may record meetings with customers, candidates, lawyers, suppliers or the board. Risks concern consent, confidentiality, retention and data location.
AI is used to analyze contracts, reports, specifications, technical documents, CVs, offers, invoices and project documentation. Often without checking whether this data may leave a controlled environment.
Employees translate commercial, legal, HR, technical and financial documents in public AI tools. This may disclose trade secrets or NDA-covered content.
Developers use AI to generate code, analyze bugs, refactor, write tests and explain repository fragments. Risks concern source code, secrets, vulnerabilities and licenses.
Employees upload spreadsheets, CRM exports, sales data, customer data, financial data and operational data to prepare summaries, forecasts or charts.
Support and sales teams use AI to draft replies, summarize tickets, translate communication or suggest next actions. Risk increases when output reaches the customer without review.
Marketing uses AI for campaigns, images, product descriptions, posts, slides and market analysis. Risks include content rights, false claims, customer data and brand compliance.
An employee may connect a private AI tool to mailbox, calendar, files or messenger to automate tasks. This is an especially risky form of Shadow AI.
A department buys an AI tool for recruitment, sales, analytics, customer service or marketing without legal, security and data protection assessment.
The most obvious risk is sending confidential data to a tool that has not been assessed. This may include customer data, personal data, contracts, code, strategies, prices, proposals and technical information.
The company may not know how long the tool stores prompts, outputs, files, transcripts and conversation history. It may also not know whether the user can delete data and whether the company administrator has control.
Some tools may use user data to improve the service if proper settings or contracts are not in place. For confidential data, this is a serious problem.
An employee’s public account usually gives the company no control over data processing terms, subprocessors, data location, audit, incidents and data deletion.
AI may generate a response that sounds credible but is wrong. If it reaches a customer, offer, legal analysis or business decision, the problem is not only technical.
Personal data, customer data and confidential information may be subject to contractual or legal limitations. Shadow AI may cause the company to violate these limitations without board awareness.
Pasting code, logs, API keys, tokens, passwords or vulnerability information into an AI tool may create real risk for the product and infrastructure.
If an employee uses AI to analyze external documents, emails or websites, a malicious instruction hidden in the content may manipulate AI output.
The company may not know who used the tool, which data was entered, what output was produced and whether the result was used in a decision or customer communication.
If AI affects recruitment, employee assessment, commercial offers, complaint handling, financial recommendations or customer communication, the company must have process oversight and accountability.
A ban may be necessary for high-risk data and unassessed tools, but a ban alone rarely solves the problem. Employees use AI because they have real needs. If the company provides no safe alternative, some usage will move to private accounts, private devices and informal processes.
A better approach combines several elements:
Shadow AI should be treated as a governance and work design problem, not only as a disciplinary problem.
The company should know which AI tools are already used. This is not about hunting employees, but about understanding the scale and type of risk.
The register should include:
Evidence to prepare: AI tool register, including approved, pilot and informally detected tools.
Not every AI use has the same risk. Drafting an internal note without confidential data is different from analyzing a customer contract or source code.
Divide use cases into:
Evidence to prepare: AI use case list with risk rating.
The AI policy should be short, understandable and practical. After reading it, an employee should know what is allowed, what is prohibited and where to ask for approval.
The policy should include:
Evidence to prepare: AI usage policy and employee acknowledgment.
Employees need concrete examples. A generic “do not paste confidential data” statement is often not enough.
Evidence to prepare: prohibited data list and department-specific examples.
If the company bans public tools but provides no approved solution, employees will find a workaround. A safe alternative reduces pressure for Shadow AI.
An approved tool should have:
Evidence to prepare: approved AI tool list and security configuration.
The approval process cannot take months if teams need AI for work. A slow process increases Shadow AI.
The process should answer:
Evidence to prepare: AI tool request form and approval path.
An AI supplier should undergo basic assessment, especially if the tool will be used with company, customer or personal data.
Check:
Evidence to prepare: AI supplier assessment and risk acceptance decision.
Shadow AI training should be concrete and team-specific. Employees should see examples from their own work, not only a general AI presentation.
Training should cover:
Evidence to prepare: training materials, participant list and knowledge test result.
Shadow AI monitoring should be lawful, proportionate and transparent to employees. The goal is not to monitor every conversation, but to detect risky tools, data flows and policy violations.
Possible information sources:
Evidence to prepare: Shadow AI risk review report without excessive intrusion into employee privacy.
The company should know what to do if an employee pasted customer data into an unapproved tool, an AI tool disclosed data, a supplier reported an incident or AI generated harmful customer output.
The playbook should cover:
Evidence to prepare: AI incident response playbook and tabletop exercise report.
HR may use AI to analyze CVs, write job ads, summarize interviews, create HR documents and communicate with employees. Risks involve personal data, bias, incorrect candidate assessment and confidential employee information.
Finance may use AI to analyze invoices, reports, forecasts, cash flow, contracts and supplier data. Risks involve financial data, contractor data, payments, wrong analysis and confidential commercial terms.
Sales may use AI for offers, emails, customer analysis, meeting summaries and inquiry responses. Risks involve customer data, pricing, margins, contract terms and communication inconsistent with the offer.
Marketing may use AI for content, graphics, campaigns, market analysis and presentations. Risks include copyright, false claims, customer data, deepfake and brand compliance.
IT may use AI for scripts, configuration, log analysis, documentation and troubleshooting. Risks involve disclosure of architecture, production logs, vulnerabilities, secrets and configuration details.
Developers may use AI to write code, tests, refactor and analyze bugs. Risks involve source code, secrets, licensing, code quality and vulnerabilities in generated fragments.
Support may use AI for replies, ticket summaries and customer history analysis. Risks involve customer data, wrong answers, disclosure of internal information and automation without human control.
The best companies do not start with accusations. They start with conversation and a safe reporting channel. Employees should be able to say they use an AI tool without immediate fear of punishment.
The goal is to move AI out of the shadows into a controlled process.
The policy should be understandable for employees outside IT. The more legalistic the language, the less likely employees will use it in practice.
The employee should know which tool they can use and for which tasks.
Give concrete examples: contracts, customer data, code, API keys, HR data, financial documents, vulnerability information.
Show that AI is not prohibited everywhere. For example, AI may be used to edit a neutral text if it contains no confidential data.
If a department wants to use a new tool, it should have a clear request path and fast response.
AI output must be checked by a human. The employee remains responsible for content they send, publish or use in a decision.
If someone pasted confidential data into an unapproved tool, they should know where to report it. The company should encourage quick reporting, not hiding mistakes.
No reports does not mean no usage. It often means employees do not know where to report tools or fear consequences.
A ban may reduce visible use, but increase private and uncontrolled use of AI tools.
Employees need concrete examples: what must not be pasted, which tools are approved and when approval is needed.
An AI tool used with company data should be assessed for retention, model training, data location, subprocessors and incident handling.
If nobody owns the tool, nobody is accountable for data, access, configuration, costs and risk.
HR, finance, sales, IT, development and marketing use AI differently. Training should reflect real scenarios for each team.
If an employee has nowhere to request a new tool, they will likely use it outside the process.
When data enters an unapproved tool, the company must know how to assess scope, preserve evidence, contact the supplier and decide on communication.
A consulting company with 120 employees has no official AI tool. Employees privately use different chatbots for email writing, translation and document summarization. One consultant pastes a customer contract excerpt into a public tool to prepare a summary for the team.
After the case is reported, it turns out there are more similar situations. HR uses AI for document drafting, sales for offers, marketing for presentations and developers for code analysis. The company has no policy, tool list, supplier assessment or data rules.
The board decides not to introduce a blind ban. The company launches a controlled AI programme. It creates a tool register, approves one company solution, enables SSO and MFA, configures retention, prepares a prohibited data list, trains teams and launches a new tool request process.
After 90 days, the company still uses AI, but most use has moved to the approved tool. Employees know what must not be pasted, and the board has an AI risk dashboard and a plan for further AI governance development.
ccyber.io helps companies move Shadow AI from uncontrolled usage into a safe, measurable and managed AI programme. We do not start from a ban. We start by understanding how employees really use AI, which data is at risk and which tools should be approved.
We can support your organization with:
The best first step is a Shadow AI Readiness Workshop. In a short workshop, the company can determine which AI tools are used, which teams are most exposed, which data must be protected, which uses can be approved and which must be limited immediately.
Shadow AI is the use of AI tools without organizational knowledge, approval or control. It may include private AI accounts, unapproved apps, plug-ins, transcription tools, content generators and AI agents.
Not always. AI use often shows a real business need. The problem is lack of control over data, supplier, retention, outputs, accountability and auditability.
Not always. In many cases, providing an approved tool, clear rules, training and a process for new requests is better. A ban without an alternative may increase hidden AI use.
Customer data, personal data, contracts, HR data, financial data, source code, secrets, vulnerability information, strategies, offers and NDA-covered data.
Start with surveys, workshops, tool register, SaaS expense review and conversations with teams. Technical monitoring can help, but should be proportionate, transparent and lawful.
Quickly determine what data was shared, which tool was used, whether deletion is possible, what the legal and business impact is, and whether the customer, DPO, supplier or regulator must be contacted.
No. A policy is necessary, but it must be combined with approved tools, training, reporting process, supplier assessment, data controls and an AI incident playbook.
Start with an AI tool register, short policy, prohibited data list, team survey, approved safe alternative and training for high-risk employees.
Shadow AI will not disappear by itself. Employees use AI because the tools genuinely help them write, analyze, translate, code and support customers. The problem starts when the company does not know which tools are used, which data enters them and who owns the risk.
The best approach is not a blind ban. It is moving AI from the shadows into a controlled process: tool register, AI policy, approved solutions, prohibited data list, supplier assessment, training, risk monitoring and AI incident procedure.
A company that manages Shadow AI well does not block innovation. It gives employees a safe way to use AI while protecting customer data, confidentiality, reputation, compliance and business accountability.
AI Security for Companies: What to Secure Before Deploying Generative AI Tools
Before deploying generative AI tools, a company should secure data, accounts, access, integrations, prompt injection, RAG, AI agents, logs, suppliers, usage rules, human oversight and incident response. The biggest mistake is treating AI like a normal SaaS application. Generative AI may process confidential data, generate wrong outputs, perform actions and affect customers, employees and business decisions.
Prepared by: CCyber Editorial Team
Before deploying generative AI tools, a company should secure not only the AI tool itself, but the full process in which AI will be used. The most important areas are: input data, output data, user permissions, supplier access, system integrations, logs, data retention, prompt injection, RAG, AI agents, output validation, human oversight, employee training and incident response. Generative AI should not be deployed by “turn it on and see what happens”. First define the purpose, owners, allowed data, prohibited use cases, risk level, security controls and evidence showing the deployment is controlled.
July 2026
AI security before deployment means preparing the company so that a generative tool can be used without uncontrolled risk to data, customers, employees, reputation, systems and business decisions.
This is not only about settings in an admin panel. It is about the full AI usage context:
Simply put: AI security combines governance, data protection, cybersecurity, access control, human oversight, testing, training and incident response.
A generative tool does not only store or process data according to predictable rules. It can generate new content, summarize documents, interpret instructions, search a knowledge base, write code, respond to customers, perform tasks and integrate with other systems.
This creates new questions:
That is why generative AI should not be deployed only as another application. It should be treated as a new risk layer in business processes.
First, the company should define why it is deploying a generative tool. Without a clear purpose, AI quickly becomes a chaotic tool used for everything, without control and without metrics.
Define:
Evidence to prepare: AI use case card with purpose, owner, data, risk and scope.
Input data is everything a user or system sends to AI: prompt, document, email, file, transcript, code, customer data, HR data, ticket, attachment or CRM record.
Before deployment, define which data may be entered into AI.
Evidence to prepare: data classification and list of data prohibited in AI tools.
AI output should not automatically be considered true, safe and ready to use. AI may generate errors, invent sources, miss context, disclose data or create content that violates company policy.
Secure output through:
Evidence to prepare: AI output validation procedure before business use.
The AI tool should be integrated with company identity management where possible. The worst model is separate accounts, private logins, no MFA and no ability to remove access after employee departure.
Check:
Evidence to prepare: AI tool access report and administrator list.
Shadow AI means using AI tools without the company’s approval, knowledge or control. It is one of the biggest risks because employees may paste confidential data into tools nobody assessed.
Reduce shadow AI through:
Evidence to prepare: register of approved and unapproved AI tools.
The AI supplier may process company data, provide the model, maintain infrastructure, store conversation history, use subprocessors or offer integrations with other systems. It must be assessed before deployment.
Check:
Evidence to prepare: AI supplier assessment and risk acceptance decision.
Before launching the tool, check data storage, history, use of data for service improvement, export, deletion, logs and administrator access settings.
In practice, check:
Evidence to prepare: AI tool privacy and retention configuration card.
Prompt Injection is an attack where a malicious instruction hidden in a prompt, document, email, website, ticket, knowledge base or file manipulates AI behavior. This is especially important for tools that analyze external content.
Controls before deployment:
Evidence to prepare: Prompt Injection test report and remediation action list.
RAG allows AI to answer based on company documents. It is useful, but risky if the knowledge base contains outdated, confidential, poisoned or poorly labeled documents.
Secure RAG through:
Evidence to prepare: AI knowledge base management procedure and RAG source register.
An AI agent is riskier than a chatbot because it can perform actions: send email, change a CRM record, create a ticket, retrieve a file, call an API, run a script or update a document.
Before deploying AI agents, define:
Evidence to prepare: AI agent permissions card and list of actions requiring human approval.
The highest risk appears when AI is connected to email, CRM, ERP, code repositories, drives, messengers, HR systems, ticketing, APIs or databases.
Before integration, check:
Evidence to prepare: AI integration, access scope and token register.
Developers often use AI to write, analyze and improve code. This can accelerate work, but may also leak code, keys, secrets, vulnerabilities or architecture information.
Define rules:
Evidence to prepare: safe AI use rules for developers.
The company should know who uses AI, for what purpose, with which data and which actions were performed. Without logs, incident analysis, audit response and policy improvement are difficult.
Logs should include, where technically and legally possible:
It is not always useful or lawful to log full prompt content. Balance security, privacy and trade secrets.
Evidence to prepare: AI usage logging policy and available log report.
Generative AI should support employees, but it should not make high-risk decisions without control. The greater the impact on customers, employees, money, access or reputation, the stronger human oversight should be.
Human oversight should be required for:
Evidence to prepare: list of AI decisions and actions requiring human approval.
Employees need simple rules. An AI policy should not be a legal document nobody reads. It should clearly say what is allowed, what is prohibited, which data is forbidden and where to report doubts.
The policy should include:
Evidence to prepare: AI usage policy and employee acknowledgment.
Without training, the policy will not work. Employees must know which data they can enter, what is prohibited, how to verify outputs, how to recognize risky situations and when to report a problem.
Training should include:
Evidence to prepare: training materials, participant list and knowledge test.
The company should have a plan for cases where AI discloses data, generates harmful content, is manipulated by prompt injection, performs a wrong action or the AI provider reports an incident.
An AI incident playbook should include:
Evidence to prepare: AI incident response playbook and tabletop exercise report.
The board should approve AI usage strategy, risk appetite, AI policy, budget, data scope and reporting rules. It does not need to decide on every tool, but it should approve the framework.
IT and security should assess the supplier, access, integrations, logs, configuration, testing, monitoring, incident response and ability to disable the tool.
Legal, compliance and DPO should assess personal data, contracts, processing basis, data transfers, information duties, retention, supplier responsibility and AI Act requirements where applicable.
HR should be especially careful with AI use in recruitment, employee assessment, personal data analysis, HR document creation and internal communication.
Finance should secure AI use in invoices, analysis, payments, forecasts, reports and supplier data. AI should not independently approve transfers or bank account changes.
These teams need clear rules on when AI can prepare a draft and when a human must approve the response. Customer data, confidentiality and alignment with offers are especially important.
Marketing should check content rights, AI content labeling, brand consistency, information sources, deepfake risk and the risk of publishing false claims.
Developers should have rules for using AI with code, tests, documentation, vulnerability analysis and repository work. AI-generated code should pass normal tests and reviews.
Employees use different tools because the company did not explain what is allowed, what is forbidden and which tool is approved.
Pasting contracts, customer data, code, technical documentation or HR data without supplier assessment and privacy settings may create serious risk.
AI may generate answers that are convincing but wrong. Outputs used for decisions and external communication require verification.
An AI tool without SSO, MFA, roles, offboarding and logs creates a risk similar to other SaaS applications, but with greater impact on data.
AI connected to email, files, CRM and APIs without minimum permissions may disclose or modify data in ways that are hard to control.
The company deploys a chatbot, RAG or AI agent but does not check whether external content can manipulate system outputs or actions.
After an incident, the company does not know who used AI, which data was entered, what was generated and whether the response reached a customer.
Automation without human approval for high-risk actions may lead to wrong decisions, data leakage or customer harm.
The company accepts tool terms without checking retention, model training, data location, subprocessors, incidents and data deletion options.
When AI discloses data or performs a wrong action, the company only then decides who blocks the tool, contacts the supplier and informs customers.
A services company wants to deploy generative AI for sales, customer service and marketing. Initially, the plan is simple: buy licenses and encourage employees to use the tool. After a short review, it turns out that teams want to paste customer inquiries, contracts, correspondence history, CRM data and offer drafts into AI.
The company pauses the fast rollout and prepares a controlled pilot. It creates an AI policy, prohibited data list, tool register, supplier assessment, SSO and MFA configuration, retention rules, user training and output validation process. AI may prepare draft responses, but a human must approve content before sending it to customers.
In the next stage, the company tests prompt injection, reviews RAG and decides that the knowledge base may contain only approved documents with owners. CRM integration initially works in read-only mode. The AI agent cannot independently change customer data or send messages.
The result: the company does not block AI. It deploys it faster and more safely because employees have clear rules, an approved tool, training, data controls and an incident response process.
ccyber.io helps companies securely prepare for generative AI deployment. We do not start from tool selection. We start from data, processes, risks, access, integrations and accountability.
We can support your organization with:
The best first step is a Generative AI Security Readiness Workshop. In a short workshop, the company can determine which AI tools are already used, which data is at risk, which use cases are safe to start with, what must be blocked, which settings must be configured and how to prepare for controlled deployment.
Not always. It is better to provide approved tools, clear rules and training. A full ban may increase shadow AI if employees continue using tools privately.
Start with an AI tool register, AI usage policy, data classification, supplier assessment, SSO, MFA, logs, training and a low-risk pilot.
Only if the company has assessed the tool, legal basis, contract, retention, privacy settings, risk and usage purpose. By default, customer data should be treated as high risk.
It can prepare drafts, but external responses should be approved by a human, especially if they involve contracts, pricing, complaints, customer data, legal matters or security.
It is an attack where a malicious instruction manipulates AI behavior. It may be entered directly by a user or hidden in a document, email, website, ticket or knowledge base.
RAG can be secure if the company controls sources, document owners, data classification, access, freshness, source citations and tests against poisoned documents.
Yes, but only with minimal permissions, logs, limits, human approval for high-risk actions and the ability to shut it down quickly.
AI usage policy, AI tool register, risk assessment, supplier assessment, data classification, integration list, logging rules, training plan and AI incident playbook.
AI security for companies is not one setting in an admin panel. Before deploying generative tools, secure data, access, supplier, integrations, RAG, AI agents, prompt injection, logs, human oversight, policies, training and incident response.
The biggest risk appears when a company deploys AI quickly, without owners, data rules, supplier assessment and integration control. In that case, AI may become a channel for data leakage, wrong decisions and uncontrolled automation.
The best approach is a controlled pilot, clear policy, approved tools, training, minimal permissions, output validation and measurable evidence. This helps the company use generative AI faster without losing control over data and processes.
IEC 62443 in Practice: Zones, Conduits and OT Network Segmentation
IEC 62443 helps organize OT security by dividing the environment into zones and communication conduits. A zone groups assets with similar risk and security requirements, while a conduit describes controlled communication between zones. In practice, this is not only about VLANs and firewalls, but about understanding processes, dependencies, risk, remote access, data flows, accountability and secure IT/OT communication rules.
Prepared by: CCyber Editorial Team
IEC 62443 in practice helps design OT security by dividing a plant, lines, systems and connections into zones and communication conduits. A zone is a group of assets with similar function, risk and security requirements, for example a production line, SCADA system, engineering workstations, process safety network or industrial DMZ. A conduit is a controlled communication path between zones, for example MES to SCADA, historian to DMZ, remote vendor to jump server or SCADA to PLC. OT network segmentation is not only about VLANs. It is a business and technical process: asset inventory, communication mapping, risk analysis, boundary definition, traffic limitation, monitoring, change management and testing.
July 2026
IEC 62443 is a series of standards for cybersecurity of industrial automation and control systems. In practice, it is used to organize security of OT, ICS, SCADA, PLC, DCS, HMI, MES, engineering workstations, historians, industrial networks and supplier remote access.
IEC 62443 is especially useful because it does not treat OT like a normal office network. It considers:
For this article, the key practical concepts are: zones, conduits, security levels, risk and segmentation.
A zone is a group of assets with similar function, risk and security requirements. A zone may be a physical part of the plant, a logical network area, a system group or a process function.
Examples of zones:
The key rule: zones are not defined only by IP addressing. A zone should follow risk, process function, criticality, ownership, trust level and incident impact.
A conduit is a controlled communication path between zones. It describes which systems can communicate, using which protocol, in which direction, for what purpose, through which control point and under which conditions.
Examples of conduits:
A conduit should have an owner and rules. Without that, the company does not know whether a given traffic flow is needed, safe and aligned with the process.
Answers the question: which assets have similar security requirements and should be treated together?
Answers the question: which communication between zones is needed and how do we control it?
Answers the question: how do we technically and organizationally separate zones and enforce communication rules?
Example: the SCADA network is a zone, SCADA to historian is a conduit, and segmentation is the firewall, allowlist, monitoring and rule that the historian cannot initiate traffic to PLCs.
In OT, one uncontrolled access path can have greater consequences than in classic IT. An attacker may start from a normal office computer, move through an administrator account, reach an engineering workstation and then affect control, quality, availability or process safety.
Segmentation helps limit:
Segmentation does not guarantee complete security, but it strongly reduces the impact of errors, incidents or compromise of one account.
A VLAN can be part of segmentation, but a VLAN alone is not enough. If there is no traffic control, monitoring, rules, owners and change process between VLANs, segmentation is only a logical network split, not a real security control.
In practice, OT segmentation means combining architecture, traffic rules, access control, monitoring, procedures and accountability.
You cannot design zones without knowing which assets exist. Inventory should include not only servers and computers, but also controllers, network devices, visualization systems, engineering workstations and remote access.
List:
Evidence to prepare: OT asset register with owner, location, function and criticality.
Segmentation cannot be designed only from the network perspective. You must know what happens in the physical process if communication is blocked, delayed or manipulated.
Ask:
Evidence to prepare: map of processes, lines and operational dependencies.
The most important segmentation document is the communication map. It shows who talks to whom, using which protocol, in which direction and for what purpose.
For every flow, record:
Evidence to prepare: IT/OT communication flow matrix.
Group assets by function, risk and security requirements. Do not mix systems with completely different criticality in one zone only because they are in the same cabinet or subnet.
Evidence to prepare: zone list with justification and owners.
After defining zones, specify which communication between them is allowed. The default rule should be: block everything that is not required and documented.
For each conduit, define:
Evidence to prepare: conduit matrix between zones.
Zones should have security requirements based on risk. Not every zone needs the same level of protection. A process safety zone, engineering workstations and critical production controllers will usually need stronger controls than a reporting zone.
Requirements may include:
Evidence to prepare: security requirements card for every zone.
Only now is it time for technology. First process, assets, zones and conduits. Then firewalls, VLANs, routing, jump servers and monitoring.
Technical controls may include:
Evidence to prepare: OT segmentation architecture design.
In OT, a wrong firewall rule may stop a line or break communication with a critical system. Testing is mandatory.
The test should verify:
Evidence to prepare: segmentation test report and exception list.
OT segmentation becomes outdated quickly if every outage, project or new line adds exceptions without control. Every conduit change should follow a process.
The process should include:
Evidence to prepare: change management procedure for OT zones and conduits.
Segmentation is not a one-time project. Traffic must be monitored, rules reviewed, exceptions removed, maps updated and real traffic compared with documentation.
Minimum maintenance:
Evidence to prepare: recurring OT segmentation maintenance report.
Includes email, office computers, user systems, internet, corporate applications and typical IT services. It should not have direct access to control systems.
A buffer between IT and OT. It can host data exchange systems, transfer servers, historian proxy, patch repository, jump server, monitoring and controlled integration points.
May include MES, production planning, reporting, quality systems and production integrations. This is often a boundary area between IT and OT.
Includes SCADA, HMI, OT application servers, internal historian and process supervision systems.
Includes PLCs, RTUs, controllers, industrial communication devices and systems directly involved in control.
Includes process safety systems, machine safety, SIS, ESD or other systems whose compromise may affect people, environment or installation. This zone requires especially careful separation.
Includes controlled supplier access through MFA, VPN, jump server, session recording, time-bound approval and logging.
Includes engineering workstations, PLC programming tools, SCADA configurations, project backups and maintenance environments. This is one of the riskiest zones because it enables changes in control systems.
Purpose: production plan, orders, materials, statuses.
Risk: ransomware or IT error may affect production planning.
Control: industrial DMZ, restricted protocols, data validation, no direct ERP connection to PLCs.
Purpose: orders, production parameters, process status.
Risk: incorrect data may affect process or quality.
Control: flow allowlist, validation, logs, direction limitation.
Purpose: supervision, commands, status reading.
Risk: unauthorized commands or operating mode changes.
Control: zone separation, industrial protocol monitoring, limiting communication sources.
Purpose: transferring production data to reporting.
Risk: uncontrolled traffic from IT to OT.
Control: preferred direction from OT to DMZ, no initiation from IT to control systems, monitoring.
Purpose: service, diagnostics, supplier support.
Risk: external access to OT environment.
Control: MFA, time-bound approval, jump server, session recording, no standing access.
Purpose: programming, diagnostics, configuration changes.
Risk: program change, incorrect configuration, malware on workstation.
Control: dedicated workstation, change control, project backup, logging, access only for authorized people.
Traffic between zones should be blocked unless it is explicitly allowed and justified.
Allow only the necessary source, destination, protocol, port and direction.
Every conduit should have a business or technical owner.
Remote access, updates, backup and engineering work should not mix with control traffic.
If data must pass between IT and OT, use DMZ, broker, intermediate server or another controlled mechanism.
Zone boundaries are natural monitoring points. Unusual traffic between zones should be visible.
A rule change can stop a process. In OT, testing and maintenance windows are more important than fast emergency changes.
Addressing and VLANs matter, but they do not replace process, risk, zone and conduit analysis.
Many plants have one large OT network where SCADA, HMI, PLCs, engineering workstations and suppliers see too much. This increases incident impact.
Direct connections from ERP, office laptops or reporting systems to OT create an easy attack path.
A DMZ is often the best place for controlled data exchange between IT and OT. Without it, integrations become random and hard to control.
A supplier should have access only when needed, through a controlled path and with logging.
If the company does not know which traffic is required, it cannot safely close unnecessary connections.
In OT, a rule change may have production impact. Every change must be tested and documented.
If nobody owns a zone, nobody makes decisions about risk, changes and exceptions.
Segmentation blocks some traffic, but monitoring shows whether someone is trying to cross zone boundaries and whether rules work.
OT networks change with every project, modernization and service action. Documentation must live with the change process.
A manufacturing plant has one large OT network. SCADA, HMI, engineering workstations, historians, several production lines and supplier remote access operate in the same address space. IT connects to MES and reporting. Documentation is outdated and some system traffic has no owner.
During the first review, the company discovers that a supplier can connect to more than one line, an engineering workstation has access to many PLCs, the historian communicates directly with the office network and the reporting system queries OT sources without a DMZ.
The company does not start by replacing the whole network. First, it creates a map of assets, flows, zones and conduits. It separates the SCADA zone, engineering workstation zone, production line zones, industrial DMZ and supplier access zone. Then it defines conduits, limits traffic, introduces a jump server for suppliers, removes unused connections and logs boundary traffic.
After 90 days, the plant still has a lot of work, but the biggest risks are visible. Instead of one flat network, it has the beginning of a zones and conduits architecture, documentation, owners and a further segmentation plan.
ccyber.io helps industrial companies translate IEC 62443 into a practical zones, conduits and OT segmentation project. We do not start from buying a firewall. We start from processes, assets, flows, risk and production dependencies.
We can support your organization with:
The best first step is an IEC 62443 Zones and Conduits Workshop. In a short workshop, the company can determine which zones exist today, which conduits are critical, where the biggest risks are, which connections should be restricted and how to prepare a realistic segmentation roadmap without stopping production.
A zone is a group of OT or IT/OT assets with similar function, risk and security requirements. It may include a line, SCADA system, engineering workstations, DMZ or process safety systems.
A conduit is controlled communication between zones. It defines who can communicate with whom, using which protocol, in which direction, for what purpose and under which conditions.
No. A VLAN can be part of segmentation, but traffic rules, firewalls, owners, monitoring, documentation, testing and change process are also needed.
Start with OT asset inventory, process map, communication flows, supplier remote access and IT/OT connections.
Not always in the same form, but in many plants it is a very good way to control data exchange between IT and OT without direct access to control systems.
Ideally the process or system owner, such as production, maintenance, OT, quality or IT. Security supports, but the owner must understand process impact.
At least annually and after every major change, modernization, new line, new supplier, incident or architecture change.
Not every project needs to end with certification. Many companies start with practical gap analysis, zones, conduits, risk management and roadmap, then decide whether formal assessment or certification is needed.
IEC 62443 helps organize OT security through a risk-based approach, zones and communication conduits. A zone answers which assets have similar security requirements. A conduit answers which communication between zones is needed and how it should be controlled.
In practice, OT segmentation is not only a network project. It is a joint project for production, OT, IT, security, maintenance, quality and suppliers. It must consider the physical process, availability, human safety, remote access, legacy systems, backup, monitoring and change management.
The best first step is not buying another firewall, but creating a map: assets, zones, conduits, flows, owners, risks and quick actions that reduce the biggest risk. Only then does technology support a real OT security architecture.
How to Prepare the Scope and Budget of a Cybersecurity Project for a Grant Application?
A strong cybersecurity project scope and budget for a grant application should be based on risk, gaps, business requirements, regulations and measurable outcomes. A list of tools or a generic “cybersecurity improvement” statement is not enough. You need work packages, actions, costs, schedule, suppliers, own contribution, eligibility, evidence and impact: lower risk, stronger resilience, safer product or audit readiness.
Prepared by: CCyber Editorial Team
The scope and budget of a cybersecurity project for a funding application should be prepared so that the evaluator, board and finance team immediately see the project logic: problem, risk, actions, costs, outcomes and evidence. The most common mistake is adding a random list of tools, training and audits without showing why they are needed and what they will change. A strong project starts with an assessment, gap map, risk register and priorities. It is then divided into work packages such as MFA, backup, monitoring, incident response, secure SDLC, SBOM, training, documentation and testing. Every cost should have an owner, justification, result, timeline, pricing basis and acceptance evidence.
July 2026
Project scope answers: what exactly the company will do, what the project does not include, which problems it solves, which results will be produced and who is responsible for them. In cybersecurity, scope should not be too general. “Improving company security” is not enough because it does not show which specific risk will be reduced.
A good scope should identify:
Simply put: scope explains what will change in the company after the project. If, after completion, the company does not have a new capability, process, control, documentation, skill or evidence, the scope is too weak.
The project budget is not only a sum of costs. It is a logical justification of how much it costs to achieve the project outcomes. The budget should show which costs are necessary, optional, potentially eligible, require own contribution and must be maintained after project completion.
A good budget should include:
The key rule: every budget item should answer “what will be created because of this cost?”.
Many companies start with: “how much funding can we get and what can we buy?”. This is the wrong approach. It leads to an artificial project that looks like a shopping list. A better process starts with risks and gaps.
The grant should fund a sensible project. The project should not pretend to be sensible only because a call appeared.
A cybersecurity project should begin with a business problem, not technology. The evaluation committee and board must understand why the project matters.
Example problems:
Evidence to prepare: short business note: problem, impact, risk and urgency.
The project may follow from regulations, contracts, customer audits, cyber insurance, board strategy or a real incident. This should be named because requirements help justify scope.
Check whether the project concerns:
Evidence to prepare: map of requirements and business pressure sources.
Gap analysis shows the difference between the current state and target state. Without it, the project may be too broad or random.
Gap analysis should identify:
Evidence to prepare: gap table with priority, risk, action and cost.
Not every project can be funded in full. It is useful to prepare two scope levels: minimum and extended.
These are actions that must be done regardless of the grant because they reduce the most urgent risk.
Examples:
These actions build stronger capability and may be a good funded project scope.
Examples:
Evidence to prepare: project variants: minimum, extension and actions outside the project.
Work packages organize scope and budget. They prevent the project from looking like a random cost list.
Evidence to prepare: work package structure with objectives, results and costs.
Every package should end with a specific result. If a package has no result, its cost will be hard to justify.
Evidence to prepare: result list and acceptance method.
The application should show how the company will measure impact. Metrics do not need to be complicated, but they must be concrete.
Example metrics:
Evidence to prepare: baseline and target indicator table.
Do not enter one “cybersecurity” item. Split the cost into concrete actions and results.
Example:
Every cost should belong to the project structure. If a cost does not fit any package, it likely needs to be removed or the scope must be adjusted.
Example: an SCA tool should be in the secure SDLC and product security package, not in generic IT cost.
Different programmes treat cost types differently. First divide costs into categories.
Then check whether the category is eligible in the specific call.
The budget should be credible. Offers, market research, price lists, specifications or benchmarks are useful.
For each item, prepare:
In cybersecurity, many costs repeat after the project ends. They should be shown even when they are not funded.
Evidence to prepare: project budget and post-project maintenance budget.
The application must be financially consistent. Before submission, check with accounting or a grant advisor how VAT should be treated, what own contribution is required, whether state aid or de minimis applies and which limits are relevant.
Check:
The budget must match the schedule. Some costs may be incurred only after eligibility starts, after signing the agreement or after supplier selection is completed.
The cost schedule should show:
Every cost should have evidence that it was incurred and the result was produced. This matters for reporting, audit and the board.
Example evidence:
The structure below helps organize the project before preparing the application.
Work package: Incident response and business continuity.
Action: incident response plan preparation and ransomware tabletop exercise.
Risk: the company has not exercised ransomware response and does not know how quickly systems can be recovered.
Result: response plan, ransomware playbook, emergency contact list, exercise report and remediation action list.
Evidence: documents, participant list, exercise scenario, report and board decisions.
Cost name: Web application and API security test.
Description: test of SaaS application and API covering authentication, authorization, access control, application vulnerabilities and recommendation report.
Result: test report, vulnerability list, priorities, retest after fixes.
Acceptance evidence: test report, acceptance protocol, retest report.
Justification: action supports product security, B2B customer requirements, secure SDLC and readiness for CRA or audit.
The cost description should be concrete. Avoid generic names that do not say what will actually be done.
“Cybersecurity services”.
“Cybersecurity audit”.
“Cybersecurity gap analysis covering Microsoft 365, backup, access control, incident response, suppliers and evidence documentation, ending with a gap report, priorities and 12-month action roadmap”.
“Scanner purchase”.
“Purchase of a vulnerability scanning tool”.
“Implementation of a vulnerability management process with a tool, scan configuration, ticketing integration, triage rules, board reporting and process effectiveness test”.
In the budget, you do not sell a tool. You show the capability the company will build through the project.
Good scope:
Typical results:
Good scope:
Typical results:
Good scope:
Typical results:
Good scope:
Typical results:
Good scope:
Typical results:
Cost eligibility must be checked for the specific call. General knowledge that “a similar cost was funded before” is not enough.
If the answer is unclear, mark the cost as “to be verified” and do not build the whole project around that item.
The schedule should be realistic. Cyber projects often require a sequence: diagnosis, design, tool selection, implementation, testing, training and evidence.
Cybersecurity should not be described only in technical language. The application should show what the project gives the business.
A good description connects technology with risk and business outcome. Example: “backup implementation” is a technical action. “Tested ability to recover the sales system within a time acceptable to the business” is a business result.
Evidence must be planned before the project starts. If the company starts collecting it only during reporting, it will be too late.
The application should show that the company understands delivery risks. This increases project credibility.
Before submission, the board should approve not only the amount, but also the project logic. Otherwise, after receiving funding, the company may struggle with delivery.
The most important board question is: which actions will we perform even if we do not receive funding?
“Cybersecurity improvement” is not enough. Specific systems, processes, actions and results must be identified.
Tools must be linked to process, users, implementation, training, metrics and evidence.
If the result of a cost cannot be shown, the budget item is weak.
Not every necessary cost can be funded. The budget should mark what needs verification.
Licenses, monitoring, scanning, backup, testing and training must continue after the project. The board must understand those costs.
A budget without pricing evidence looks less credible and may be hard to defend.
Cyber projects require decisions, access, integrations, testing and acceptance. The schedule must be realistic.
A cyber project cannot sit only with IT. If it affects product, manufacturing, sales, customers or board risk, business owners must be involved.
A SaaS company wants to submit an application for cybersecurity funding. The first budget version has five items: vulnerability scanner, EDR, phishing training, penetration test and consulting. The project looks like a shopping list and it is hard to assess what exactly will change after delivery.
After scope cleanup, the company starts with risk. Enterprise customers require security evidence, the product will need CRA readiness, and the current development process has no SBOM, SCA or formal vulnerability management. The company also has no exercised incident response plan.
The new project scope has four packages: secure SDLC and SBOM, vulnerability management, incident response and customer evidence. The budget is linked to results: product SBOM, implemented SCA, API test report, vulnerability handling procedure, tabletop exercise, developer training and customer evidence pack.
The result: the project is no longer a tool purchase. It becomes a product security and B2B sales readiness programme. This scope is easier to justify to the board, customers and funding institution.
ccyber.io helps companies prepare the scope and budget of cybersecurity projects for funding applications, board decisions or investor discussions. We combine risk, technology, regulation, product, finance and audit evidence perspectives.
We can support your organization with:
The best first step is a Cybersecurity Project Scope and Budget Workshop. In a short workshop, the company can define what should enter the project, what should stay outside, which costs are critical, which results must be shown and how to prepare a budget that is credible from both business and technical perspectives.
No. First define risks, gaps and results. Tools should follow from the scope, not be the starting point.
Detailed enough to assess cost reasonableness, eligibility, result and acceptance evidence. One item named “cybersecurity” is too general.
Yes, even if they will not be funded. The board and funding institution should see that the company understands project sustainability and post-funding costs.
It depends on the call and company readiness. Often, one coherent programme with work packages works well, but some actions such as training may have a separate funding path.
Yes, but it increases the risk of weak scope and poor budgeting. Gap analysis helps show why the project is needed and which actions are priorities.
Do not justify the purchase alone. Justify the process the tool enables: vulnerability detection, monitoring, access control, backup, incident response or audit evidence.
Implementation, configuration, team training, integrations, testing, license maintenance, log retention, acceptance, documentation and remediation actions after tests.
Start with a short assessment, risk list, minimum scope, work packages and high-level budget. Then check the specific call and cost eligibility.
A strong cybersecurity project scope and budget for a funding application must show the logic: problem, risk, gap, action, cost, result, metric and evidence. Without this, the project looks like a shopping list or compliance formality.
The best cyber projects are divided into work packages and build real company capability: better access control, more effective backup, faster incident response, safer product, secure development, vulnerability management, monitoring, training and audit evidence.
The key rule is: the budget should follow the scope, the scope should follow risk, and risk should follow the company’s real business. Then the project is easier to defend to the board, funding institution, auditor and customer.
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.
Every article delivers practical knowledge - no jargon, just concrete steps you can act on to effectively protect your data, people and business continuity.