🔴Illinois HB 3773IN EFFECTUp to ~$70K/violation|🔴Texas TRAIGA (HB 149)IN EFFECTAG-enforced|🔴Utah AI Policy ActIN EFFECT$2,500/violation|⚠️Colorado AI Act (SB 205)Jan 1, 2027AG-enforced|⚠️California SB 942Aug 2, 2026$5K/day|⚠️EU AI Act Art. 50Aug 2, 2026€35M or 7% revenue|⚠️New York RAISE ActJan 1, 2027AG civil penalties|
European Union · EU AI ActNo AI-specific statute — measured, not assumed (2026-09-02)

Israel AI Compliance Requirements

Mandatory and recommended controls under EU AI Act + national rules, including the role of the local data-protection authority.

Deadline: No AI-specific compliance deadline exists, because no AI-specific statute exists. The obligations that reach AI systems are the standing ones under the Protection of Privacy Law and its Data Security Regulations, which are already in force.Penalty: No AI-specific penalty. Searched 2026-09-02 against the Knesset's own national legislation register: no primary statute in force carries בינה מלאכותית (artificial intelligence) in its title, so there is no Israeli AI offence to state. Amounts under the Protection of Privacy Law are deliberately NOT quoted here: the operative sections were not read at source in this pass, and a fine figure taken from anywhere but the statute is exactly the kind of number this product does not publish.

How AI law works in Israel

Israel has no artificial-intelligence act, and that is a measurement rather than an impression: the Knesset's national legislation register was queried directly on 2026-09-02 and returns no primary law in force whose title contains בינה מלאכותית. What binds an AI deployer in Israel is therefore general law. The Protection of Privacy Law, 5741-1981 has been in force since 11 March 1981 and governs the personal data an AI system is trained on and processes. Underneath it, the Privacy Protection (Data Security) Regulations, 5777-2017 were published in Reshumot on 8 May 2017 and set the security duties that attach to holding a database — Israel's regulation layer for this subject is real and is secondary legislation, not an AI statute. Biometric processing has its own primary legislation: the 2009 Act on including biometric identifiers in identity documents and a database, and a further Act of 28 July 2024 on taking biometric identifiers from foreign nationals — both in force, and both directly relevant to facial-recognition and identification systems. Competition exposure runs through the Economic Competition Law, 5748-1988, which is the route an algorithmic-pricing or self-preferencing matter would take. ⚠️ One limit, stated plainly: the register is searched by TITLE, so an Israeli instrument that governs AI without naming it in its title would not appear — the claim here is that these named statutes exist and are in force, not that this is an exhaustive inventory of every AI-relevant provision in Israeli law.

Applicable laws

  • 📜 Protection of Privacy Law, 5741-1981 (חוק הגנת הפרטיות)
  • 📜 Privacy Protection (Data Security) Regulations, 5777-2017 (תקנות הגנת הפרטיות (אבטחת מידע))
  • 📜 Economic Competition Law, 5748-1988 (חוק התחרות הכלכלית)

EU AI Act requirements begin with system risk assessment. Your organization must evaluate every AI system against the EU AI Act's risk framework: prohibited systems (facial recognition in law enforcement, social credit scoring, subliminal manipulation), high-risk systems (hiring, benefits determination, law enforcement, biometric ID), limited-risk systems (chatbots), and minimal-risk systems (game AI, spam filters). The legal requirement is to classify your system correctly. Misclassification — for example, claiming that a hiring AI is minimal-risk when it is high-risk — is itself a compliance violation. High-risk classification triggers the heaviest compliance burden: conformity assessment, bias and fairness testing, documented risk mitigation, human oversight, transparency, and record-keeping. If you are uncertain whether a system is high-risk, the safe assumption is to treat it as high-risk and apply the full compliance framework.

Pre-deployment conformity assessment is the core requirement for high-risk systems. Before deploying a high-risk AI system (or immediately, if it is already deployed), you must complete a documented assessment covering: data quality — are the training and decision-making data representative of the population affected by the system, and do they contain known biases?; model performance — does the model perform equally well across demographic groups, or is accuracy lower for protected groups?; system explainability — can you explain to an affected individual why the system made a particular decision?; human oversight design — what process allows an individual to escalate the AI decision to human review?; and risk mitigation — what controls have you implemented to reduce the risk of discriminatory outcomes? This assessment must be documented in writing, reviewed by qualified personnel, and updated at least annually.

Bias and fairness testing is a specific requirement for high-risk systems. The EU AI Act does not prescribe a particular testing methodology, but requires that your organization conduct documented testing and be able to demonstrate that you have evaluated the system for discriminatory impact across protected characteristics (race, color, religion, national origin, sex, gender identity, sexual orientation, disability, age, etc.). Testing must include: hold-out test data not used in training, representative of the affected population; evaluation of decision-rate parity across groups (does the AI approve loans at the same rate for all genders, races, and age groups?); and performance parity testing (does the AI make accurate predictions equally well across all groups?). Document test results, identify any disparate impact, and implement mitigation (rebalance training data, adjust decision thresholds, redesign features, or limit the system's scope).

Transparency and human-rights mechanisms are mandatory for all systems, with intensity scaling to risk level. For limited-risk systems (chatbots), you must disclose that the individual is interacting with AI. For high-risk systems, transparency is much deeper: you must inform affected individuals before the AI system makes a decision about them, explain what data the system is using, describe how the system works (at a non-technical level accessible to the individual), and provide the individual with a clear, accessible process to request human review and appeal the AI decision. In employment and benefits contexts, individuals must be able to request re-evaluation by a human reviewer, and that human review must be genuine — a human who has authority to override the AI decision and the information needed to make an independent judgment.

Ongoing monitoring, record-keeping, and individual-rights response are permanent obligations. You must monitor every high-risk AI system's performance and decisions on an ongoing basis (not just at deployment). Maintain audit logs of every high-risk decision for at least three years, capturing inputs, decision outputs, confidence scores, human-review flags, and any human override. When an individual requests an explanation of an AI decision, you must respond within 30 days with accessible, non-technical information about how the system works and why it made that particular decision. When an individual requests appeal or human review, you must provide it. Failure to respond to individual rights requests is a documented compliance violation and a source of private civil liability.

Israel compliance requirements, ranked

Mandatory under EU AI Act + GDPR

Risk classification per national framework + GDPR-equivalent DPIA
Public AI-use disclosure to end-users in their language
Human review path for adverse automated decisions (GDPR Art. 22)
Cooperation with Israel's supervisory authority + serious-incident reporting
Records sufficient to reconstruct each automated decision (3+ years)

Strongly recommended

Annual third-party bias / fairness audit
AI vendor due-diligence questionnaire (training data provenance, sub-processors, retention)
Cross-functional AI governance committee
Public-facing complaint mechanism

Country context

Applicable framework: Protection of Privacy Law, 5741-1981 (חוק הגנת הפרטיות)
Applicable framework: Privacy Protection (Data Security) Regulations, 5777-2017 (תקנות הגנת הפרטיות (אבטחת מידע))
Applicable framework: Economic Competition Law, 5748-1988 (חוק התחרות הכלכלית)
Regulator: see official sources block below for Israel's primary AI / data-protection authority.
Status: No AI-specific statute — measured, not assumed (2026-09-02). Headline penalty exposure: No AI-specific penalty. Searched 2026-09-02 against the Knesset's own national legislation register: no primary statute in force carries בינה מלאכותית (artificial intelligence) in its title, so there is no Israeli AI offence to state. Amounts under the Protection of Privacy Law are deliberately NOT quoted here: the operative sections were not read at source in this pass, and a fine figure taken from anywhere but the statute is exactly the kind of number this product does not publish..

More Israel resources

AI Compliance Checklist💰 AI Law Fines & Penalties📖 AI Compliance Guide AI Law Deadlines← All Israel resources

Other countries

Germany (EU)France (EU)Netherlands (EU)Spain (EU)Italy (EU)Sweden (EU)
Editorial standards

Anchored to the primary government source (statute, bill text, or agency rule) and verified directly against it · Last verified Sep 2, 2026. See our methodology.

Primary sources · Israel
  • knesset.gov.ilhttps://knesset.gov.il/OdataV4/ParliamentInfo/KNS_IsraelLaw(2000234)
  • knesset.gov.ilhttps://knesset.gov.il/OdataV4/ParliamentInfo/KNS_SecondaryLaw(2067455)
  • knesset.gov.ilhttps://knesset.gov.il/OdataV4/ParliamentInfo/KNS_IsraelLaw(2000247)