be executed by authorized users . This not only protects against unauthorized access but also ensures the integrity of the Python code . AxProtector Python ‘ s File Encryption Mode also allows for the secure encryption of AI models .
In native environments where AI applications are developed in languages such as C ++ and others , or models are transformed into native code using LLVM , AxProtector Compile Time Protection ( CTP ) offers a comprehensive solution for obfuscation , alongside encryption and signing . This technique intentionally alters the code to make it nearly unreadable to humans while preserving the functionality of the program . This makes it difficult for attackers to analyze or manipulate the code , providing an essential shield against reverse engineering . Just like AxProtector Python , AxProtector CTP offers protection techniques that ensure code and models are only used by authorized users , and any changes to the code are immediately detected .
Licensing and Monetization : AI as a Protected Economic Asset
The question of how companies can monetize their AI models is becoming increasingly important . Businesses that invest significant resources into developing AI models must ensure that they protect their investments and retain control over the use of their technologies . This is especially relevant when AI models are not sold as open products but are provided to customers as licensed solutions .
AxProtector Python enables companies to not only protect their AI models but also to license them strategically . The File Encryption Mode allows companies to strictly bind access to AI models to CodeMeter licenses . This means that both the code and the data can only be used by authorized users , significantly enhancing the protection of intellectual property . This type of licensing allows companies to use their AI models as a recurring revenue stream , with customers potentially purchasing regular licenses to access and use the models .
In a similar way , AxProtector CTP offers a solution for native AI applications . By binding the native code to a license , it ensures that only authorized users can utilize the models . Additionally , obfuscation makes it significantly more difficult to analyze the code , even in the event of unauthorized access .
These licensing models open up new monetization opportunities for companies offering their AI models both as finished products and as services . By implementing such security measures , companies can ensure that their models are used only as intended and prevent unauthorized use .
The Role of Encryption and Signing in Regulatory Compliance
The growing importance of AI across various sectors is leading to increased regulation . Frameworks such as the EU AI Act and the Cyber Resilience Act ( CRA ) require companies to ensure that their AI systems are not only efficient but also secure . In particular , highrisk AI systems must meet strict security requirements to protect against unauthorized access and manipulation .
Products like AxProtector Python and Ax- Protector CTP assist companies in complying with these regulatory demands . AxProtector Python secures AI models and Python scripts through encryption and ensures their integrity with digital signatures . AxProtector CTP provides similar protection mechanisms for native applications and models transformed via LLVM . Both solutions help meet the requirements of the Cyber Resilience Act , particularly in the areas of confidentiality , integrity , and availability ( CRA Annex 1 , Section 1.3 b , c , and d ), as well as the provisions of the EU AI Act .
Protection Against Attacks : Encryption and Signing as Security Strategies
AI models and applications are increasingly targeted by cyberattacks such as model theft and model poisoning . Model theft involves unauthorized attempts to copy or use the model , while model poisoning aims to alter the model ’ s behavior by manipulating its parameters .
Encryption serves as a defense against these attacks by restricting access to the model only
to authorized users , thereby preventing unauthorized usage . Licensing through encryption keys ensures that models are used only within the intended framework .
Additionally , digital signing guarantees the integrity of the model . Any modification to the model would invalidate the signature , signaling potential tampering and thus enhancing protection against model poisoning , ensuring the correctness of the model ’ s parameters .
With AxProtector Python and AxProtector CTP , companies can protect their AI applications through comprehensive and powerful protection technologies . These solutions help prevent model theft and defend against model poisoning by ensuring the integrity and confidentiality of the models .
Conclusion : The Path to a Secure AI Future
The development and proliferation of AI create new opportunities but also introduce new risks , particularly concerning security and the protection of intellectual property . Encryption , obfuscation , and targeted licensing offer effective strategies to safeguard AI models against unauthorized access and manipulation .
Products like AxProtector Python and Ax- Protector CTP provide companies with comprehensive tools to protect their applications and AI models . These solutions not only ensure security but also enable the strategic monetization of AI applications while meeting regulatory requirements . By deploying such technologies , companies are better protected against attacks and can ensure that their products thrive in an increasingly regulated and competitive world .
5