AI governance software helps organizations manage the policies, controls, documentation, risk classification, review workflows, and audit evidence needed for artificial intelligence systems. Smart Integrity Platform supports AI system inventory, risk classification, model governance, audit workflows, documentation review, and AI regulatory compliance.

An AI compliance platform helps teams organize AI systems, classify risk, document controls, assign review tasks, and prepare evidence for internal or regulatory review. SIP connects AI documentation from tools such as Confluence and SharePoint with governance workflows and audit-ready reporting.

AI audit software helps organizations collect, organize, and review evidence related to AI systems. SIP supports audit workflows by linking source documentation, model information, risk questionnaires, human review steps, and compliance records in one platform.

AI inventory software helps organizations create and maintain a list of AI systems used across the business. SIP supports AI inventory workflows by connecting existing documentation, identifying system details, and helping teams track purpose, ownership, risk tier, and review status.

High-risk AI systems should bear the CE marking to indicate their conformity with this Regulation so that they can move freely within the internal market. For high-risk AI systems embedded in a product, a physical CE marking should be affixed, and may be complemented by a digital CE marking. For high-risk AI systems only provided digitally, a digital CE marking should be used. Member States should not create unjustified obstacles to the placing on the market or the putting into service of high-risk AI systems that comply with the requirements laid down in this Regulation and bear the CE marking. Before starting, you must determine if your AI is “High-Risk.” Annex III Categories: Includes AI for recruitment (CV screening), credit scoring, education (grading), or critical infrastructure. Annex I Products: If your AI is a safety component of a product already requiring a CE mark (e.g., medical devices or machinery), it is automatically high-risk.

To pass the assessment, your AI Governance must prove compliance in these areas:

1. Risk Management System (Art. 9): A continuous process to identify and mitigate risks.

2. Data Governance (Art. 10): Proving your training/testing data is “relevant, representative, and free of errors.”

3. Technical Documentation (Art. 11 & Annex IV): A massive file (often 200+ pages) detailing the architecture, algorithms, and energy consumption.

4. Human Oversight (Art. 14): Designing the UI so a human can “stop” or override the AI at any time.

How you get the mark depends on your AI’s specific use case: Internal Control (Self-Assessment): For most Annex III systems (like HR or Credit tools), you can perform the assessment yourself—provided you follow “Harmonized Standards” (like ISO 42001).

Third-Party Assessment (Notified Body): Mandatory for biometric identification or if you don’t follow harmonized standards. You must hire an EU-authorized “Notified Body” to audit your tech.

Drawing up the “EU Declaration of Conformity”Once you’ve proven compliance, you sign a formal legal document (the DoC) stating that your system meets all AI Act and other relevant EU laws (like the Cyber Resilience Act). You take full legal responsibility with this signature.

Affixing the CE Marking (Art. 48) Digital Products: For SaaS, the CE mark must be visible in the digital interface (e.g., on the login screen or “About” section).

Physical Products: If embedded in hardware, it must be on the product or packaging.

ID Number: If a Notified Body was involved, their 4-digit ID number must appear next to the CE logo.

Registration in the EU Database Before the AI goes “live,” you must register the provider and the system in the official EU Database for High-Risk AI Systems.

1. August 2, 2026: This is the “Hard Deadline.” After this date, any new high-risk AI system placed on the market must have a CE mark.

2. The “Notified Body” Bottleneck: If you need a third-party audit, start now. As of early 2026, wait times are exceeding 12 months due to a shortage of authorized auditors.

In a legal sense, AI Governance under the AI Act is a mandatory system of internal controls and accountabilities designed to ensure that artificial intelligence is safe, transparent, and compliant throughout its entire lifecycle. It shifts AI oversight from voluntary ethical guidelines to a binding “product safety” framework, requiring organizations to maintain rigorous risk management, data quality standards, and human oversight. For “High-Risk” systems, this governance is the legal prerequisite for securing a CE marking, involving permanent audit trails and technical documentation to prove regulatory alignment. Ultimately, it serves as the operational “operating system” that connects corporate responsibility with technical transparency to prevent algorithmic harm and massive financial penalties.

The EU AI Act and GDPR are concurrent frameworks: while the GDPR protects personal data privacy, the AI Act functions as a product safety law for the systems themselves. They overlap significantly in high-risk areas like automated decision-making, where companies must often conduct both a Data Protection Impact Assessment (DPIA) and a Fundamental Rights Impact Assessment (FRIA). Crucially, the AI Act includes a specific “carve-out” allowing the processing of sensitive data to detect and correct algorithmic bias, which is otherwise restricted under the GDPR. Compliance is not “either/or”—to avoid fines reaching up to 7% of turnover in 2026, organizations must ensure their AI governance satisfies both sets of regulations simultaneously.

Under the AI Act, chatbots must be designed to immediately inform users that they are interacting with an AI, ensuring transparency from the very first interaction. If the system generates synthetic text, images, or audio, this content must be marked in a machine-readable format to remain detectable as AI-produced. Additionally, if your chatbot uses emotion recognition to adjust its responses based on the user’s mood, this specific capability must be clearly disclosed. Failing to meet these transparency standards by the August 2026 deadline can result in significant fines of up to €15 million or 3% of global turnover.

The EU AI Act establishes a tiered fine structure, where the most severe violations involving prohibited AI practices can reach €35 million or 7% of global annual turnover. For non-compliance with “High-Risk” obligations or transparency rules, companies face penalties up to €15 million or 3% of turnover, while providing misleading information to regulators can cost up to €7.5 million. To protect innovation, the law applies a “whichever is lower” principle for SMEs and startups, ensuring that fines are proportionate to the size of the business.

Companies outside the EU ask if they are affected simply because their output is used by someone inside the EU (the answer is almost always yes).

An AI system inventory is a structured record of AI systems, models, use cases, owners, documentation, risk levels, and compliance status. It helps organizations understand where AI is used and which systems may require further review, controls, or documentation.

AI risk classification is the process of categorizing AI systems by risk level based on their purpose, use case, impact, and regulatory requirements. SIP supports AI risk classification workflows for prohibited, high-risk, limited-risk, and minimal-risk AI systems.

AI compliance software supports EU AI Act workflows by helping teams classify AI systems, document risk controls, manage human oversight evidence, prepare technical documentation, organize audit records, and review high-risk AI system requirements.

AI governance tools support model governance by helping teams document model purpose, data sources, technical controls, risk assessments, ownership, approvals, monitoring needs, and review decisions. SIP connects these records with broader compliance workflows.

Model risk management for AI systems means identifying, assessing, controlling, and monitoring risks that may arise from AI model design, data quality, deployment, bias, performance, or misuse. SIP supports model risk management through risk questionnaires, documentation review, and task workflows.