AI Security & Governance

AI SECURITY AND GOVERNANCE

Use AI as a productivity tool without exposing corporate data uncontrolled.

Secure Fors establishes policies, technical controls, training and monitoring mechanisms for the secure use of ChatGPT, Copilot, Gemini, Claude, coding assistants, meeting summarizers and similar AI tools. The goal is to protect personal data, trade secrets, customer information, source code and corporate documents without blocking employee productivity.

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KVKK Generative AI Guide Shadow AI DLP/CASB/SWG Prompt Security AI Governance
Secure AI Usage Zone
EXAMPLE
01
Approved AI Tools Corporate account, contract and data processing terms
ALLOWED
02
Data Classification Personal data, trade secrets, customer data, code
RULE
03
Technical Control DLP, logging, access, browser and network policies
CONTROL
04
Training and Monitoring Correct prompting, output validation, incident reporting
CONTINUOUS
AI inventory
DLP data control
KVKK compliance focus
%88 Stanford AI Index 2026’ya gre kurumların AI kullanımı 2025’te en az bir iş fonksiyonunda %88’e ulaştı.
%63 IBM 2025’e gre birçok kurum shadow AI kullanımını ynetmek için AI governance politikasına sahip değil.
KVKK KVKK’nın 2026 üretken AI metni, iş yerlerinde kontrolsüz AI kullanımının veri güvenliği ve mahremiyet risklerini vurguluyor.
24/7 AI tools are open to continuous data contact via browser, plug-in, mobile application, meeting tool and code editor.
WHY NOW

This is no longer optional; employees already use AI and data can leave the company.

Completely banning generative AI is not sustainable for most organizations. The real need is to create a secure usage zone where employees gain productivity while corporate data, personal data and security controls remain protected.

Shadow AI reduces corporate visibility. When employees start using AI with personal accounts or with tools without corporate approval, it cannot be monitored which data is entered where, which output turns into a business decision, and which supplier is involved in the process.
Personal data sharing requires control in terms of KVKK. When meeting notes, customer correspondence, human resources documents, call center records, health/financial data or employee information are entered into AI tools, data processing conditions, purpose limitations, lighting and security measures come into question.
Information security is not ensured only by the "prohibited word list". When data classification, DLP, access control, corporate accountability, vendor evaluation, logging, incident reporting and training are not designed together, the policy will not be implemented in the field.
AI output also produces risk. Risks such as hallucination, missing sources, copyright risk, wrong code recommendation, automation bias and prompt injection must be managed not only when data is entered into the tool, but also when the output is taken into the business process.
CONSULTING SCOPE

We build a secure AI usage model inside your organization.

The goal of this service is not to stop the use of AI. The goal is to create a controlled AI usage model by clarifying secure tools, permitted data types, technical controls, employee codes of conduct, and monitoring mechanism.

DISCOVERY AND INVENTORY

Shadow AI Analysis

We make visible which AI tools are used, by which teams, and for which data and processes.

  • Detection of approved and unapproved AI tools
  • Use case, team and data theme map
  • Browser, plug-in, SaaS, meeting and code assistant uses
  • Classification of risky data entry points
Output: AI kullanım inventory, glge AI risk haritası ve ncelikli aksiyon listesi.
KVKK AND DATA SECURITY

Data Usage Rules

We determine how personal data, trade secrets, customer information, source code and internal documents can be used with AI tools.

  • Data classification and sharing matrix for AI
  • Risk assessment of personal data and sensitive data
  • Data types that should not be shared in the prompt
  • KVKK, confidentiality and contractual liability control
Output: AI data usage matrix, KVKK risk rating and internal usage rules.
TECHNICAL CONTROLS

DLP, Access and Monitoring

We design security technologies, access controls and monitoring setup so that the policy can be implemented in the field.

  • Company hesap, SSO/MFA ve yetki modeli
  • DLP, CASB/SWG, browser and endpoint control recommendations
  • Logging, event notification and exception management
  • Certified AI tools and vendor security assessment
Output: Technical control architecture, tool rubric and monitoring model.
PROMPT AND OUTPUT SECURITY

Secure Usage Practices

Çalışanların AI’dan faydalanırken yanlış veri paylaşımı, hatalı çıktı ve security zafiyeti üretmesini azaltacak çalışma kuralları oluştururuz.

  • Secure prompt guide and example usage templates
  • Hallucination, source verification and human confirmation rules
  • Code, document, legal, sales and human resources scenarios
  • Prompt injection and data leak awareness
Output: Secure prompt guide, output verification flow and user scenarios.
TRAINING INCLUDED

Employee and Executive Training

AI security isn't just documented. Teams need to learn what they can do, what they can't do, and what to report in case of suspicion.

  • Productive AI safe use awareness
  • KVKK and personal data sharing risks
  • Secure prompt, output validation and event notification
  • AI risk and governance summary for the management team
Output: Training sessions, materials, attendance records and awareness outcomes.
POLİTİKA VE CONTINUOUS IMPROVEME

AI Usage Policy

We prepare the policies, roles, approvals, exceptions and review processes that will make the institution's use of AI sustainable.

  • Productive AI usage policy
  • Role and responsibility matrix
  • Approved vehicle list, exception and request process
  • Periodic review and improvement reporting
Output: Company AI kullanım politikası, süreç akışları ve ynetim raporu.
ROADMAP

We establish a safe AI usage area step by step.

The program makes current usage visible rather than prohibiting it; controls risky data flows; It creates the institutional framework where employees can use AI safely and efficiently.

01 Start and scope Target teams, AI tools used, business processes, and security expectations are clarified.
02 AI usage discovery Approved and unapproved AI tools, user groups, and data touchpoints are identified.
03 Data and KVKK analysis Personal data, trade secrets, customer information and special risky data sharing scenarios are evaluated.
04 risk assessment Information security, cybersecurity, supplier, output accuracy and operational risks are prioritized.
05 Policy and rules Approved tools, prohibited data types, exception process and employee usage rules are created.
06 Technical control design Target architecture is prepared for DLP, CASB/SWG, access, logging, network and endpoint controls.
07 Pilot application Safe AI usage area is implemented in selected teams, problems and exceptions are observed.
08 Education and dissemination Employees are given training on secure prompting, data sharing, output verification and incident reporting.
09 Monitoring and improvement Usage metrics, incidents, exceptions, and new AI tools are reviewed regularly.
THE SECURE FORS DIFFERENCE

We make AI security applicable across law, information security and cybersecurity.

Safe AI use is not just a warning text prepared by the legal team or a blocking rule by the information security team. User behavior, personal data, supplier terms, technical security controls, training and ongoing monitoring must work together.

Secure Fors combines information security, KVKK compliance, supplier security, cybersecurity and AI governance experience in the same work plan. Thus, the use of AI within the organization provides both efficiency and a manageable, traceable and defensible framework.

Consulting Deliverables

  • AI kullanım inventory ve glge AI risk haritası
  • AI use risk assessment focused on KVKK and data security
  • Approved AI tools and third-party vendor rubric
  • Productive AI usage policy and data sharing matrix
  • DLP, CASB/SWG, access, logging and monitoring control recommendations
  • Secure prompt guide and output validation rules
  • Employee and manager training materials
  • Pilot implementation, monitoring metrics and improvement report

The use of AI may have started in your organization; The important thing is to make it visible and safe.

Which teams are using which AI tools, what data is being shared, what outputs translate into business decisions, and what controls are missing? You can clarify your secure AI usage area roadmap by making a preliminary assessment with Secure Fors.

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Statistics and guide sources: Use of Productive Artificial Intelligence Tools in KVKK Workplaces, Stanford AI Index 2026, IBM Cost of a Data Breach 2025.