Privacy & Security

Project KARL is engineered from the ground up with user privacy and data security as its foremost design principles. This section elaborates on the architectural choices and operational guarantees that underpin KARL's commitment to a privacy-first AI model. Understanding these details is crucial for both developers integrating KARL and end-users who will benefit from its on-device intelligence.

The KARL Privacy Model

KARL's privacy model is built upon the foundational concept of keeping user data and AI processing strictly localized to the user's own device. This inherently minimizes many of the privacy risks associated with traditional cloud-based AI systems.

Zero Data Egress by Default

The default operational mode of a KARL container ensures zero data egress. This means that no InteractionData used for learning, nor any derived model parameters or user-specific learned patterns, are transmitted off the user's device to any external servers or third parties. The entire learning loop – data input, feature extraction, model training (trainStep), and inference (predict) – occurs within the local environment managed by the KarlContainer.

Any potential future features involving synchronization of learned AI state (e.g., for backup or transfer between a user's own devices) would require explicit, granular user opt-in, employ end-to-end encryption where the user controls the keys, and would focus on syncing the abstract model state, not the raw interaction logs. The core library, however, does not mandate or include such features by default.

Local Processing Guarantee

All computational tasks related to KARL's adaptive learning and prediction generation are executed on the user's device. This includes:

  • Feature Engineering: Transformation of raw InteractionData (provided by the application's DataSource) into numerical representations suitable for the machine learning model within the LearningEngine.

  • Model Training: The incremental updates to the local AI model's parameters based on new interactions.

  • Inference: The use of the locally trained model to generate predictions or suggestions.

This local processing eliminates the need to send potentially sensitive behavioral data to external servers, thereby mitigating risks of unauthorized access during transit or on third-party storage. It also ensures that the AI features can function offline.

Encryption and Secure Local Storage (Implementation Responsibility)

While the KARL core interfaces (like DataStorage) define the contract for persistence, the responsibility for implementing encryption at rest for the locally stored KarlContainerState (which includes the serialized AI model) and any cached InteractionData lies with the specific DataStorage implementation module (e.g., :karl-room).

It is strongly recommended that these implementations leverage robust encryption mechanisms:

  • For SQLite-based storage (like Room): Utilize SQLCipher or similar full-database encryption libraries. Key management for this encryption should ideally leverage platform-specific secure elements or keystores (e.g., Android Keystore System, macOS Keychain) to protect the encryption keys themselves.

  • File-based state storage (if applicable): Employ standard file encryption techniques using cryptographically secure libraries.

The choice of encryption library and key management strategy is an implementation detail of the DataStorage module and the hosting application, but KARL's design philosophy mandates that such protection is in place for production deployments.


Developers implementing or choosing a DataStorage module should refer to its specific documentation for details on its encryption capabilities and setup, for example, the security considerations for the
:karl-room implementation.

Strict API Access Control via the KarlContainer

The KarlContainer acts as a gatekeeper to the AI's learned state and processing capabilities. Applications interact with KARL's intelligence solely through the defined public API of the KarlContainer instance (e.g., initialize(), getPrediction(), saveState(), reset(), release()).

The internal components like the LearningEngine and the raw data within DataStorage are not directly exposed to the application. This encapsulation ensures that data flow is controlled and adheres to the intended operational model, preventing inadvertent data leakage or misuse of the learned model outside its defined scope.

User Control over KARL

Empowering users with control over their data and the AI that learns from it is a key tenet of Project KARL. Applications integrating KARL should expose mechanisms for users to manage their KARL instance.

Implementing User Instruction/Rules (The KarlInstruction & InstructionParser Concept)

KARL's architecture includes the concept of KarlInstruction objects, which allow applications (and by extension, users) to define rules or preferences that modify the behavior of their local AI container. Examples include:

  • Instructing KARL to ignore certain types of InteractionData for learning.

  • Setting a minimum confidence threshold for predictions to be surfaced.

  • Temporarily pausing learning.

The application would be responsible for providing a UI for users to specify these rules (e.g., through settings toggles, a simple text-based DSL). An implementation of the InstructionParser interface would then convert this user input into a list of KarlInstruction objects, which are then passed to the KarlContainer (e.g., via karlContainer.updateInstructions(...)).

For details on defining custom instructions and parsers, refer to the API Reference for KarlInstruction and InstructionParser and potentially an Example Tutorial on implementing user rules.

Viewing Learned Data/Patterns (If Feasible and Privacy-Preserving)

Transparency can enhance user trust. Depending on the nature of the LearningEngine and the data it processes, applications could potentially offer users insights into what KARL has learned about their patterns. This is a complex area:

  • Directly exposing raw model weights is generally not user-friendly or interpretable.

  • Instead, an application might translate certain learned patterns into understandable summaries (e.g., "KARL has noticed you frequently use feature X after feature Y," or "Your most common command sequence is A -> B -> C").

  • This requires careful design to ensure that any "insights" provided are genuinely helpful and do not inadvertently expose data in a way that compromises privacy or is easily misinterpreted.

  • KARL's core library does not provide this out-of-the-box; it would be an application-level feature built on top of KARL's capabilities, potentially by querying and interpreting the (anonymized) InteractionData stored by DataStorage or specific metrics exposed by the LearningEngine.

Any feature allowing users to view learned patterns must be designed with extreme care to maintain privacy and provide meaningful, non-technical explanations.

Resetting or Deleting KARL Data (KarlContainer.reset())

Users must have the unequivocal ability to reset their local KARL AI instance and delete all associated learned data. The KarlContainer.reset() method is provided for this purpose. When called:

  • The LearningEngine is reset to its initial, untrained state.

  • The DataStorage implementation is instructed to delete all persisted KarlContainerState and any associated InteractionData for that specific user/container ID.

Applications should clearly expose this functionality to users (e.g., in a privacy settings section), allowing them to effectively "forget" all local AI learning and start fresh if they choose.