01
System & ownership
Purpose, owner, lifecycle stage, integrations, and deployment model.
ציות וממשל AI · AI Compliance
הטמעת בינה מלאכותית בארגון היא כבר לא רק אתגר טכנולוגי – אלא גם אתגר עסקי, משפטי ורגולטורי. שירותי AI Compliance מספקים לארגונים את המסגרת הנדרשת לאימוץ בטוח, אחראי ומבוקר של טכנולוגיות AI, תוך שילוב בין חדשנות עסקית, ניהול סיכונים ועמידה בדרישות רגולטוריות. אנו מלווים ארגונים בכל שלבי מחזור החיים של מערכות AI – החל מגיבוש אסטרטגיית AI ומיפוי השימושים בארגון, דרך סיווג מערכות, הערכת סיכונים וניתוח פערי ציות, ועד להקמת מסגרת AI Governance הכוללת מדיניות, נהלים, מנגנוני בקרה, תיעוד, אחריות ארגונית ופיקוח אנושי.
השירות כולל ליווי בהטמעת תהליכי ממשל תאגידי לבינה מלאכותית, הכנת תיעוד וראיות ציות, בחינת ספקי AI וצדדים שלישיים, שילוב דרישות פרטיות, אבטחת מידע וניהול סיכוני מידע, וכן התאמת פעילות הארגון לדרישות רגולטוריות ולסטנדרטים בינלאומיים, לרבות ה-EU AI Act, GDPR, ISO 42001, NIST AI RMF ומסגרות ציות נוספות. בנוסף, אנו מסייעים בבניית תוכניות יישום, ביצוע ביקורות, הכשרת עובדים והנהלה, וליווי שוטף של תהליכי בקרה ושיפור מתמיד, כך שה-AI יהפוך לנכס עסקי המייצר ערך – ולא לחשיפה רגולטורית, משפטית או תפעולית.
הפתרונות מותאמים לחברות הייטק, ארגונים מסחריים, מוסדות פיננסיים, חברות תעשייה, ארגוני בריאות, גופים ציבוריים ועסקים מכל מגזר, ומאפשרים לאמץ טכנולוגיות AI בצורה אחראית, מאובטחת וברת-קיימא. מעבר לעמידה בדרישות הרגולציה, אנו מסייעים לארגונים לחזק את אמון הלקוחות, המשקיעים והשותפים העסקיים, להגן על המוניטין הארגוני ולהפחית את הסיכון לפגיעה תדמיתית, עסקית או משפטית הנובעת משימוש בלתי מבוקר בבינה מלאכותית. הגישה שלנו משלבת מומחיות עסקית, טכנולוגית ורגולטורית, ומאפשרת לארגונים להמשיך לחדש בביטחון, תוך יצירת מסגרת ממשל וציות התומכת בצמיחה עסקית לאורך זמן.
AI Governance & Compliance Consulting
Bring order, ownership and control to the AI your business already uses.
Profitec helps B2B teams map AI tools, vendors, data flows and decision points — then design the ownership, review paths, controls, documentation and operating cadence needed to adopt AI with confidence.
AI inventoryHuman oversightVendor governanceEvidence readiness
governance.workspace · examplerunning
Illustrative governance workspaceexample states · not live metrics
- AI system register
- Active
- Ownership mapping
- In review
- Vendor record
- Required
- Human oversight
- Defined
- Evidence readiness
- Building
What is AI governance consulting?
AI governance consulting puts ownership, controls and evidence around the AI you run.
AI governance consulting helps a company understand which AI systems operate across the business, what data and decisions they affect, who owns them, and which controls, review paths, evidence and operating routines should be in place.
The problem
Where unmanaged AI becomes a business problem
AI adoption is easy to start and hard to account for. These are the points where it turns into real exposure.
01
AI use expands without ownership
Tools, copilots and agents spread team by team, with no one accountable for any of them.
CostNo one can say what runs, on what data, or who is responsible.
02
Sensitive data enters unknown paths
Customer, employee and confidential data flows into AI tools and vendors before anyone reviews where it goes.
CostExposure no one signed off on, and no record of it.
03
AI affects real decisions without review
Outputs shape hiring, pricing, support and approvals with no human review or escalation path.
CostDecisions you cannot explain, defend, or correct.
04
Due diligence arrives before evidence exists
A customer questionnaire or procurement review asks how you govern AI — and there is nothing to show.
CostStalled deals and reactive scrambles.
05
Policies exist, but behaviour does not change
A policy was written once and filed; day-to-day adoption ignores it.
CostGovernance on paper, not in how teams actually work.
The methodology
How we assess an AI system
02
Intended use & boundaries
Users, allowed use, unsupported use, expected benefit, and decision impact.
03
People & oversight
Affected stakeholders, potential harms, human review, transparency, and escalation.
04
Data, vendors & deployment
Data types, sources, sharing, retention, providers, and external dependencies.
05
Risk, evidence & operating readiness
Known limitations, misuse, failure impact, control gaps, and evidence and review needs.
Governance Control Plane
From unmanaged AI activity to a governance operating model
We map the AI you run, give every system an owner and a data-and-decision context, design the controls and review paths that fit, and turn it into evidence and a review rhythm you can keep current.
governance.plane · examplehuman oversight
- 01AI tools, agents & vendors
- ChatGPT
- Copilot
- Claude
- Gemini
- Internal AI
- APIs
- 02System & owner map
- Purpose
- Accountable owner
- Lifecycle
- Integrations
- 03Data & decision context
- Data sensitivity
- Affected users
- Decision impact
- External exposure
- 04Control design
- Approval path
- Human review
- Access boundaries
- Vendor checks
- Fallback logic
- 05Evidence & operating rhythm
- AI register
- Review records
- Policy acknowledgement
- Issue log
- Periodic review
From unmanaged AI activity → to a repeatable governance operating model
Conditional review
When deeper review is required
Most AI systems need a light, consistent baseline. These conditions raise the bar — and define a specific path before the system ships or expands.
01
Sensitive or confidential data
Review data path, vendor role and access conditions.
02
Customer-facing or decision-influencing output
Define transparency, review and escalation path.
03
Autonomous or irreversible action
Require approval gate, action limits and rollback logic.
04
HR, finance, healthcare, education or other high-impact context
Expand discovery, evidence requirements and specialist review.
05
New model, vendor, feature or deployment market
Run a change review before rollout.
Supporting modules
Built in where AI does more
Two areas get extra structure when they apply — without turning governance into a separate security program.
Governance for AI agents & automations
Agents and automations that take real actions get access boundaries, approval gates on sensitive or irreversible steps, fallback logic, and an audit trail — plus a review before they go live.
Secure, reviewed deployment
Before an AI system, model or vendor goes live, we review access, data handling, and how its outputs are used — so adoption is deliberate and accountable, not a default switch-on.
What you receive
What we design and implement
Not a policy template — the working layer that makes AI adoption accountable and provable.
01
AI system and vendor inventory
Which AI tools, agents and vendors run, by whom, and on what data.
02
Ownership and accountability map
An accountable owner, purpose and lifecycle stage for each system.
03
Use-case risk and impact overview
How each use affects data, people and decisions.
04
Data and decision-flow review
Where data goes, who is affected, and what decisions AI influences.
05
Priority controls and human-oversight recommendations
Approval paths, review points and access boundaries that fit.
06
AI adoption and vendor-review workflow design
A repeatable path for adopting new tools, models and vendors.
07
Documentation and evidence structure
An AI register, review records, policy acknowledgement and issue log.
08
Phased implementation roadmap
What to put in place first, and how to operate it over time.
The engagement
From assessment to an operating model
01
Assess
Map AI systems, data, vendors, decisions and current governance gaps.
02
Prioritize
Identify where ownership, evidence, controls or review paths matter first.
03
Design
Create governance structure, documentation logic, approval flows and operating routines.
04
Implement
Embed the model into how teams adopt tools, launch AI features and review vendors.
05
Operate
Keep inventory, evidence, controls and review cadence current as AI use expands.
Fit
Where AI governance consulting fits — and where it doesn't
Best fit
- B2B companies adopting AI across multiple teams
- Businesses handling customer, employee, financial, confidential or sensitive data
- Teams shipping customer-facing AI, copilots, agents or automated workflows
- Companies responding to customer due diligence and procurement questionnaires
- Leadership teams that need ownership and evidence around AI adoption
Not a fit
- One-off chatbot demos
- Teams seeking fully autonomous AI with no review process
- Teams seeking formal certification or jurisdiction-specific regulatory representation
Common questions
What teams ask before we start.
01What is the outcome of an AI governance assessment?
A clear picture of the AI you run and a working model around it: an inventory of tools, agents and vendors; an owner, purpose and data-and-decision context for each; the controls and review paths that fit; and a documentation and evidence structure you can keep current. You leave with priorities and a phased roadmap, not a generic policy.
02Which AI tools and use cases can be assessed?
Anything in use across the business — ChatGPT, Copilot, Claude, Gemini, internal AI, API integrations, agents and automated workflows — across functions like HR, finance, support, marketing, product and operations, with deeper review where data sensitivity or decision impact is higher.
03How do you assess AI risk without turning it into a black-box score?
We assess each system in plain terms — purpose and ownership, intended use and boundaries, people and oversight, data and vendors, and known limitations and failure impact. The result is reviewable reasoning and concrete control recommendations, not an opaque number.
04When is human oversight required?
Whenever output influences a real decision, touches sensitive or confidential data, or an action is autonomous or hard to reverse. We define the review point, who owns it, and the escalation path — proportionate to the impact, not blanket friction.
05Can this cover AI agents and automated workflows?
Yes. Agents and automations that take real actions are where governance matters most. We add access boundaries, approval gates on sensitive or irreversible actions, fallback logic, and an audit trail, and we review each before it ships.
06Can governance be embedded into our existing approval and vendor processes?
Yes. The goal is a model that runs inside how you already work — tool adoption, vendor review, feature launches and procurement — so governance becomes a routine step, not a separate program.
07How does this connect to workflow automation and enterprise RAG?
The same controls, ownership and evidence apply to the AI systems we build with you. Governance is the control layer around Profitec's AI Workflow Automation and Enterprise RAG work, so what you adopt is accountable from day one.
השלב הבא
Put a working governance model around the AI you already run.
Start with a focused conversation about the AI tools, data flows, vendors and decisions that matter most to your business.