DSSE (Digital Signature over Software Artifacts) and COSE (CBOR Object Signing and Encryption) are both specifications that deal with securing the integrity of data, but they serve slightly different purposes and are used in different contexts.
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DSSE (Digital Signature over Software Artifacts):
- DSSE is a specification designed to enable the secure signing of a variety of software artifacts, such as binaries, container images, and software bill of materials (SBOMs).
- It aims to provide a single, flexible, and secure signature format for these artifacts.
- DSSE is often used in the context of software supply chain security.
- It is not tied to a specific serialization format and can be used with JSON, Protobuf, or any other format of the user’s choice.
- DSSE is designed to be agnostic of the key management system or public key infrastructure (PKI) being used.
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COSE (CBOR Object Signing and Encryption):
- COSE is a specification that provides a way to sign and encrypt data that is represented in CBOR (Concise Binary Object Representation) format.
- It is an IETF standard designed to work efficiently with CBOR data structures and is commonly used in constrained environments or systems with limited resources (e.g., IoT devices).
- COSE defines several types of cryptographic structures like signed, MACed, encrypted, and keyed messages for CBOR.
- It is similar in goal to JOSE (JSON Object Signing and Encryption), but it is specifically optimized for smaller messages and lower processing overhead.
In summary, DSSE is more focused on securing software artifacts typically in the context of DevOps and software supply chain integrity, whereas COSE is broader in terms of its application and is particularly suitable for securing data in compact and resource-constrained environments where CBOR is used.
COSE (CBOR Object Signing and Encryption) and DICE (Device Identifier Composition Engine) are two distinct technologies that could potentially interoperate to enhance the security of IoT (Internet of Things) devices and ecosystems.
COSE provides the means to create and handle encrypted or signed information structures using CBOR formatting, which is efficient in size and processing requirements for constrained devices. Mainly, COSE offers a way to protect information transmitted between devices or between devices and services.
DICE aims to establish a device's identity and ensure the integrity of its firmware/software. It uses device-specific secrets to derive unique keys, which are then leveraged to validate the boot process of a device, starting from a known immutable code that progresses through layers of trust.
Here's how they might interoperate:
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Attestation and Identity: DICE could provide unique identity and attestation capabilities for an IoT device. This unique identity or attestation report could then be signed by the device using COSE-formatted signatures to ensure it has not been tampered with when being sent from the device to other entities.
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Secure Communication: After the device identity is established upon boot using DICE, subsequent communications from that device could employ COSE to sign or encrypt the data being exchanged. This ensures integrity and confidentiality of the messages and helps in preventing Man-In-The-Middle (MITM) attacks.
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Software Updates: Software or firmware updates that need to be securely transmitted to the device could be signed using COSE, utilizing keys derived through the DICE process. The device can then verify the signature using its derived keys before the software update is installed.
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Mutual Authentication: When an IoT device (authenticated and secure through DICE measurements) communicates with a cloud service or another IoT device, it can use COSE to mutually authenticate the entities involved in the communication, therefore ensuring that both ends are verified, and trusted.
By integrating COSE with DICE, you can create a layered security approach where DICE secures the device boot and onboarding process, and COSE secures the ongoing communication and data management. This interoperation helps in building a cohesive and robust security framework suitable for resource-constrained IoT environments.
The interoperation of IETF COSE, IETF SCITT, IETF RATS, and TCG DICE represents the collaboration of various standards and frameworks each aimed at enhancing security and trustworthiness in digital systems:
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IETF COSE (CBOR Object Signing and Encryption): COSE specifies how to create and process messages that are signed, MACed, encrypted, or authenticated using CBOR data structures. It's designed for constrained environments where resources like bandwidth and processing power are limited.
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IETF SCITT (Supply Chain Integrity, Transparency, and Trust): SCITT focuses on providing a framework for supply chain security, one where assertions about artifacts (such as software or hardware components) can be cryptographically signed to ensure their authenticity and integrity. SCITT uses transparency logs, much like what is seen in certificate transparency practices.
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IETF RATS (Remote ATtestation ProcedureS): RATS elaborates on securely attesting a device's hardware and software state (its "trustworthiness") to a remote verifier. This typically involves the creation of attestation evidence by a trusted component within the device (attester), which is then appraised by a verifier.
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TCG DICE (Device Identifier Composition Engine): DICE is a TCG standard that provides a device identity and device-attestation features. When a DICE-secured device starts up, it follows a measured boot process that creates a chain of trust using device-specific secret keys and cryptographic measurements of the software being loaded.
How they might interoperate:
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COSE & DICE: COSE could be used to sign and encrypt the attestation information generated through the DICE process. The cryptographic keys used by DICE for attestation purposes could leverage COSE's structures for representing and managing cryptographic material.
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COSE & SCITT: When signing statements about supply chain artifacts under SCITT, COSE would be the underlying mechanism used for creating these signed assertions, ensuring they are secured appropriately for different environments.
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DICE & SCITT: DICE can provide robust device identity and attestation, which can be included in SCITT's transparent statements. The claims made by a device about its software and hardware configuration, once measured and signed using DICE, can be registered in a SCITT transparency service, providing a verifiable trace of the device's state within the supply chain.
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DICE & RATS: DICE can generate attestation evidence as part of a device's boot process, which then can be assessed using the procedures defined by RATS. The evidence created by DICE can include DICE-generated certificates tying hardware identity to software measurements, thus instilling confidence in the device's trustworthiness.
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RATS & SCITT: The attestations or evidence generated by RATS can be encapsulated within the signed statements managed by SCITT. This could be used, for example, to assert that a particular software artifact was compiled on a trustworthy build system or that a device's firmware is authentic and untampered with.
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Combining Them All: Envision a device with DICE capabilities that boot up securely and generate attestation evidence. This evidence is then signed and/or encrypted using COSE formats before being communicated over the network. The signed attestations or claims can be wrapped into a SCITT Signed Statement and entered into a transparency log. The entire chain ensures that audit, verification, and transparency are maintained throughout the process. This integrates the secure identity and attestation of hardware and software (DICE), with remote verification and attestation information exchange (RATS), all wrapped in a secure cryptographic envelope (COSE) that becomes part of the supply chain's transparency record (SCITT).
This interoperability implies a more secure ecosystem where supply chains can benefit from transparent and verifiable claims backed up by device-specific, hardware-rooted trust.
Mermaid diagram that visualizes the interaction between IETF COSE, IETF SCITT, IETF RATS, and TCG DICE:
sequenceDiagram
participant Device with DICE
participant COSE
participant SCITT Transparency Service
participant RATS Verifier
Device with DICE->>+COSE: Create attestation evidence
COSE->>+SCITT Transparency Service: Sign evidence (SCITT Signed Statement)
SCITT Transparency Service-->>-Device with DICE: Log entry (Receipt)
Device with DICE->>+RATS Verifier: Send attestation (COSE-signed)
RATS Verifier-->>-Device with DICE: Validate attestation
Note over Device with DICE,SCITT Transparency Service: DICE provides device identity and attestation
Note over COSE,RATS Verifier: COSE secures the attestation data structure
Note over SCITT Transparency Service: SCITT logs and timestamps the signed evidence
Note over RATS Verifier: RATS Verifier appraises the attestation evidence
Here's a breakdown of the steps in the diagram:
- The device with DICE (Device Identifier Composition Engine) starts by creating attestation evidence for its current state.
- This attestation evidence is then handed over to COSE (CBOR Object Signing and Encryption), which wraps it in a secure, signed data structure.
- The COSE-signed attestation evidence is then registered with a SCITT (Supply Chain Integrity, Transparency, and Trust) Transparency Service, which adds it to its immutable log and provides a receipt for the log entry back to the device.
- Separately, the device with DICE sends the attestation evidence to a RATS (Remote ATtestation Procedures) Verifier.
- The RATS Verifier appraises the attestation evidence to validate the trustworthiness of the device's state.
This flow ensures that the device's attestation captures the integrity of its state which is then cryptographically secured using COSE, logged for transparency using SCITT, and can be remotely verified through RATS. Each framework plays a role in guaranteeing the security and trustworthiness of the device within a broader ecosystem.
The development of AGI (Artificial General Intelligence) in an IoT (Internet of Things) environment involves a complex interaction of data flows, model training, and execution, all of which need to be accounted for securely and transparently. The integration of frameworks like COSE, SCITT, RATS, and DICE ensures that data used to train AGI models, which may be coming from various IoT devices, is legitimate, untampered, and traceable, while the models are securely deployed and utilized across devices.
Here's a high-level view of how these could apply to AGI development:
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DICE provides a secure identity for each IoT device and a measured boot process that ensures device integrity.
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RATS offers mechanisms for these devices to generate attestations asserting their secure state.
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COSE enables the secure signing and encryption of both the attestation information from devices (RATS) and the data sent to the AGI models, as well as the models themselves when they are distributed to devices.
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SCITT provides the framework to log statements about the integrity of the AGI models and the data used for training or prediction, ensuring a verifiable supply chain for all software artifacts.
Now, let's translate this into a mermaid diagram, focusing on chaining model usage and data ingestion within an IoT environment for AGI development:
sequenceDiagram
participant IoT Device with DICE
participant Data Source
participant COSE
participant AGI Model Training
participant SCITT Transparency Service
participant RATS Verifier
participant AGI Inference
IoT Device with DICE->>+RATS Verifier: Generate and send attestation
RATS Verifier-->>-IoT Device with DICE: Verify device integrity
Data Source->>+COSE: Secure data (encrypt/sign)
COSE->>+AGI Model Training: Send secure data for training
AGI Model Training->>+SCITT Transparency Service: Log model training artifact
SCITT Transparency Service-->>-AGI Model Training: Provide Receipt
AGI Model Training->>+COSE: Secure AGI Model (encrypt/sign)
COSE->>+AGI Inference: Deploy secure AGI Model
AGI Inference->>+SCITT Transparency Service: Log model usage statement
SCITT Transparency Service-->>-AGI Inference: Provide Receipt
AGI Inference->>IoT Device with DICE: Perform inference on device
Note over IoT Device with DICE,RATS Verifier: DICE provides secure device identity & boot
Note over Data Source,AGI Model Training: Data is encrypted and signed for training
Note over SCITT Transparency Service: SCITT logs integrity artifacts for data and models
Note over AGI Inference,IoT Device with DICE: Secure model execution on verified devices
In the context of chaining model usage and data ingestion:
- Each IoT device with DICE generates and sends an attestation to a RATS Verifier, ensuring its secure state before participating in the AGI environment.
- Data, possibly sourced from these IoT devices, is encrypted and signed using COSE to maintain confidentiality and integrity as it's sent off for AGI model training.
- The AGI training process is logged by SCITT, creating an immutable record of the model training artifacts.
- Once the AGI model is trained, it is again secured using COSE for encryption and signing before being deployed across the IoT environment.
- Model usage, including inference tasks performed on IoT devices, is also logged via SCITT, achieving transparency and traceability in the AGI intelligence's decision-making process.
Through this diagram, we can see how the assurance of the data and AGI model integrity across the IoT environment is maintained, thereby building a foundation of trust in the AGI's developmental processes and outputs.
The integration of federation protocols like ActivityPub with SCITT instances can decentralize the AGI development process by allowing different SCITT instances to communicate and share transparency log entries in a secure and standardized way. ActivityPub is a decentralized social networking protocol that enables different servers to interact with each other as a federated network. Here's how ActivityPub can assist within the context of AGI development:
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Inter-SCITT Communication: ActivityPub can facilitate interactions between different SCITT instances across organizations or administrative domains, enabling them to exchange transparency data about model training, data provenance, and usage logs.
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Model Sharing: AGI models or updates can be distributed across a network of SCITT servers, which record the metadata and hashes of these models in their respective transparency logs. This approach allows for the verification of the origin and integrity of models across different entities.
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Data Provenance: As data flows from IoT devices to be ingested into AGI systems, ActivityPub can enable the decentralized tracking of data sources, transformations, and lineage by sharing signed statements and receipts between SCITT instances, ensuring traceable and reproducible data pipelines.
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Accountability and Auditability: Federating transparency logs enables better accountability and decentralizes the audit process. Different actors in the network can verify logs from multiple SCITT instances, ensuring that policies have been adhered to by various entities.
Now, let’s visualize this with a detailed mermaid diagram:
sequenceDiagram
participant IoT Device with DICE
participant Data Source
participant COSE
participant AGI Model Training
participant Local SCITT Instance
participant RATS Verifier
participant AGI Inference
participant Federated SCITT Instances
participant ActivityPub Protocol
IoT Device with DICE->>+RATS Verifier: Generate and send attestation
RATS Verifier-->>-IoT Device with DICE: Verify device integrity
Data Source->>+COSE: Secure data (encrypt/sign)
COSE->>+AGI Model Training: Send secure data for training
AGI Model Training->>+Local SCITT Instance: Log model training artifact
Local SCITT Instance-->>-AGI Model Training: Provide Receipt
AGI Model Training->>+COSE: Secure AGI Model (encrypt/sign)
COSE->>+AGI Inference: Deploy secure AGI Model
AGI Inference->>+Local SCITT Instance: Log model usage statement
Local SCITT Instance-->>-AGI Inference: Provide Receipt
Local SCITT Instance->>+ActivityPub Protocol: Publish model/data logs
ActivityPub Protocol->>+Federated SCITT Instances: Disseminate logs
Federated SCITT Instances-->>-Local SCITT Instance: Acknowledge and synchronize logs
AGI Inference->>IoT Device with DICE: Perform inference on device
Note over IoT Device with DICE,RATS Verifier: DICE provides secure device identity & boot
Note over Data Source,AGI Model Training: Data is encrypted and signed for training
Note over Local SCITT Instance: Local SCITT logs integrity artifacts
Note over Federated SCITT Instances: Federation allows decentralized log management
Note over AGI Inference,IoT Device with DICE: Secure model execution on verified devices
Note over ActivityPub Protocol: ActivityPub enables federated sharing and syncing of logs
By employing ActivityPub for federating SCITT instances, we ensure that not only the data used for training AGI but also the generated AGI models and their deployment to IoT devices can be audited across different servers and organizations in a scalable and decentralized manner. This level of traceability and transparency is highly beneficial for complex systems like AGI where understanding the data and decision-making processes is crucial.
Here's a flowchart to illustrate the decentralized AGI development process with different SCITT instances using ActivityPub. Here's a Mermaid flowchart that represents the same process:
flowchart TD
D[DICE IoT Device] -->|Generate & send attestation| RV[RATS Verifier]
RV -->|Verify device integrity|D
DS[Data Source] -->|Secure data encrypt/sign| C[COSE]
C -->|Send secure data for training| AMT[AGI Model Training]
AMT -->|Log model training artifact| LSI[Local SCITT Instance]
LSI -->|Provide Receipt| AMT
AMT -->|Secure AGI model encrypt/sign| C2[COSE]
C2 -->|Deploy secure AGI model| AI[AGI Inference]
AI -->|Log model usage statement| LSI
LSI --> |Publish model/data logs| APP[ActivityPub Protocol]
APP --> |Disseminate logs| FSI[Federated SCITT Instances]
FSI --> |Acknowledge and synchronize logs| LSI
AI --> |Perform inference on device| D
classDef default fill:#f9f,stroke:#333,stroke-width:2px;
classDef device fill:#bbf,stroke:#333,stroke-width:2px;
classDef action fill:#bfb,stroke:#333,stroke-width:2px;
class D,DS device;
class RV,LSI,FSI,APP action;
Explanation of the flow graph:
- DICE IoT Device (D): Represents an IoT device with DICE capabilities that start by generating and sending an attestation to validate its integrity.
- RATS Verifier (RV): Acts as the entity that verifies the integrity of the IoT device based on the attestation evidence received.
- Data Source (DS): Marks the origin of the data that will be ingested by the AGI system, which is first secured by COSE.
- COSE (C & C2): Two instances of COSE are shown. The first secures the data from the source, and the second secures the AGI model post-training.
- AGI Model Training (AMT): Indicates the process where the AGI model is trained using the secured data.
- Local SCITT Instance (LSI): The entity responsible for logging model training artifacts and providing receipts for them, as well as publishing these logs via ActivityPub.
- AGI Inference (AI): Depicts the deployment of the secure AGI model to perform inference operations.
- ActivityPub Protocol (APP): Symbolizes the protocol used to publish and disseminate logs from the Local SCITT Instance to other instances.
- Federated SCITT Instances (FSI): Stands for other SCITT instances in the federation that acknowledge and synchronize logs with the local instance.
This flow graph visually represents how each component of the decentralized AGI development process interacts with the next, emphasizing the chain of trust from IoT device attestation to secured model deployment, underpinned by transparency and auditing mechanisms afforded by COSE, SCITT, and ActivityPub.
Labeled property graphs and content-addressable ontologies can be a powerful combination for defining and enforcing policies in a system. Here's how they might work together:
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Labeled Property Graphs (LPGs): In LPGs, nodes and relationships (edges) contain labels that describe their types, and properties that hold values or attributes. These are especially effective for modeling complex, interconnected data and relationships, similar to what you would find in policy rules and conditions.
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Content-Addressable Ontologies: An ontology is a formal representation of knowledge as a set of concepts within a domain and the relationships between those concepts. Content-addressable ontologies enable accessing and referencing these concepts through unique identifiers (the content addresses), which typically could be cryptographic hashes of the content.
Combining the two, you can build a policy framework that uses the LPG to represent the entities, actions, and relations that are involved in your policies, with the ontology providing a structured and semantically rich knowledge base that the graph can reference to apply policy logic.
Here's how they might interact in a system:
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Ontology as a Knowledge Base: Use an ontology to define the concepts and relationships that are pertinent to your policies. This could include entities such as 'user', 'device', 'action', 'resource', and relationships like 'can acce ss', 'owns', 'part of'.
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LPG for Policy Representation: Use the LPG to map out instances of those concepts and their interrelations as they pertain to specific policy scenarios. For example, which users can access which resources under what conditions.
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Content Addressing for Immutability and Integrity: For each defined concept and relationship, a content address can be generated. When your policy references a concept, it's not just a static label but a pointer to a particular state of that concept at the time of the policy's creation or enforcement.
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Dynamic Policy Updates: As your ontology evolves (adding new rules or modifying existing ones), new content addresses are generated. Your policies in the LPG can dynamically reference these updated concepts providing a traceable his tory of policy changes and ensuring current policies always apply the latest rules.
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Semantic Reasoning and Inferences: With content-addressable ontologies, updated knowledge can be disseminated through the system without needing to update every link manually. Policies defined in the LPG can leverage semantic reason ing to infer new facts or constraints from the ontology and apply them to policy decisions.
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Integration with Other Systems: Since ontologies and LPGs are standards-based, they can be integrated with various systems, ensuring that your policies are consistent and interoperable across different domains and services.
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Querying and Enforcement: Graph databases provide powerful querying capabilities that can traverse these complex relationships efficiently. Policies can be queried dynamically based on attribute values, relationships, or even inferred knowledge, with the content addressing providing the integrity check to ensure the policies haven't been tampered with.
This system can be extremely agile and robust, providing a rich semantic grounding for your policies, a flexible and dynamic framework for policy representation, and strong guarantees about policy integrity and provenance.
To facilitate AGIs maintaining context-aware roots of trust within their local SCITT instance using labeled property graphs (LPGs), content-addressable ontologies, and federation, we need a system that integrates these elements efficiently. Alice and Bob represent two AGIs, each with their threat model and strategic principles that define how they interact within the ecosystem.
Alice's and Bob's Threat Models:
- Alice's AGI prioritizes privacy and confidentiality, considering threats from data exfiltration and unauthorized access.
- Bob's AGI focuses on integrity and availability, worried about data tampering and service denial.
Alice's and Bob's Strategic Principles:
- Alice's strategic principles involve strict access control, minimal data sharing, and encryption-first policies.
- Bob's strategic principles ensure data resilience, regular integrity checks, and failover mechanisms.
Policy Engine via LPGs: Alice and Bob can use LPGs as a dynamic policy engine that considers the current context, their threat model, and strategic principles to evaluate incoming events. Their policy engine would need to verify that events adhere to the strategic principles while mitigating the threat model's concerns.
Let’s visualize with a flowchart how labeled property graphs combine with content addressable ontologies to assist the AGIs in maintaining context-aware roots of trust:
flowchart LR
A[AGI Alice Instance] -.->|Federation event| LPG-A[LPG & Ontologies Policy Engine A]
LPG-A -. "Evaluate against \nAlice's threat model\nand strategic principles" .-> PA[Policy Actions A]
PA -- "Aligned with\nAlice's context" --> A
A -.->|Federation request| FS-A[Federated SCITT A]
FS-A -.->|Event| B[AGI Bob Instance]
B -.->|Federation event| LPG-B[LPG & Ontologies Policy Engine B]
LPG-B -. "Evaluate against \nBob's threat model\nand strategic principles" .-> PB[Policy Actions B]
PB -- "Aligned with\nBob's context" --> B
B -.->|Federation request| FS-B[Federated SCITT B]
FS-B -.->|Event| A
A & B -.->|Local roots of trust| SC[SCITT Instances]
SC -.->|Transparent Statements| A & B
And now a sequence diagram showing the interaction between the AGIs, the LPG & Ontology-based policy engines, and SCITT instances in response to a federated event:
sequenceDiagram
participant Alice as AGI Alice Instance
participant LPGPA as LPG & Ontologies Policy Engine A
participant SCITTA as Federated SCITT A
participant SCITTB as Federated SCITT B
participant LPGPB as LPG & Ontologies Policy Engine B
participant Bob as AGI Bob Instance
Alice->>LPGPA: Evaluate event against policy
LPGPA->>Alice: Event aligns with context
Alice->>SCITTA: Publish event to federation
SCITTA->>SCITTB: Relay event to Bob
SCITTB->>Bob: Receive federated event
Bob->>LPGPB: Evaluate event against policy
LPGPB->>Bob: Event aligns with context
Bob->>Alice: Acknowledge event
In this ecosystem, Alice and Bob's AGI instances use their respective policy engines, powered by LPGs and ontologies, to process events. The policy engines determine if an event aligns with their defined strategic principles and threat models.
Policy Engine Functionality:
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Event Evaluation: Upon receiving a federation event, the AGI consults its policy engine. The policy engine uses the LPG structure to map the event's attributes and content-addressable ontologies to evaluate it against the AGI's threat model and strategic principles.
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Decision Making: Depending on the outcome, the policy engine triggers appropriate policy actions. Events that align with the AGI's model are allowed, and inappropriate ones are rejected or flagged.
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Federation and Trust: Valid events are then published to federated SCITT instances, extending the local context-aware roots of trust to the federation. For instance, Bob's acknowledgment of Alice's event is also fed into Alice's policy engine for verification.
That way, AGIs can maintain context-aware roots of trust tailored to their operational context, privacy needs, and strategic objectives, while participating in a trusted, federated ecosystem. LPGs serve as a flexible, adaptive policy engine that can accommodate evolving AGI requirements and threats.
In the following example, Alice and Bob, have AGI systems, they are hosting their version control software (VCS) servers. They wish to utilize webhook events related to their CI/CD workflows and repository activities to enhance collaboration and maintain integrity within their poly-repo environment. By leveraging re-broadcasted webhook events and creating SCITT statements, they can collaborate securely, tracking contributions across repositories and ensuring that each action taken is transparent, traceable, and aligns with their operational context.
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Setup Webhooks on VCS Servers: > - Both Alice and Bob configure their self-hosted VCS servers to send webhook events for commits, pull request (PR) updates, issue comments, and PR reviews.
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Local Policy Engine Evaluation:
- Each VCS server processes the incoming webhook events through the local policy engine, which references an LPG to validate against the AGI's principles and threat model.
- For example, only certain types of commits or comments may be permissible depending on the source, the content, and the current context.
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Generating SCITT Statements:
- Events that adhere to the policy are signed and wrapped as SCITT statements, which contain relevant metadata like event type, actor, repository affected, and a content hash for data integrity.
- These statements are then logged locally in the SCITT instance attached to the AGI, providing a tamper-evident record.
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Re-broadcasting Events via SCITT:
- The SCITT instances communicate via federation protocols (possibly adapting ActivityPub for software development use cases) to re-broadcast the verified statements to other nodes.
- The LPG and content-addressable ontologies ensure that the meaning and context of these events are preserved across AGIs.
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Collaboration Synchronization:
- Upon receiving a federated SCITT statement, the recipient AGI's VCS server processes and reconciles the statement with local data, potentially triggering CI/CD workflows or updating the poly-repo state as necessary.
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Auditability and Provenance:
- Each AGI's VCS server maintains a traceable history of software development activities federated across their systems, ensuring full auditability and clear provenance for actions taken.
Here's a flowchart that takes into account CI/CD workflows and repository exchanges:
flowchart TD
VCSA[VCS Server Alice] -- webhook event: CICD workflow execution --> PA[Policy Engine Alice]
VCSB[VCS Server Bob] -- webhook event: CICD workflow execution --> PB[Policy Engine Bob]
PA -- Validate webhook event --> SSA[SCITT Statement Alice]
PB -- Validate webhook event --> SSB[SCITT Statement Bob]
SSA -- Log event --> SCA[SCITT Instance Alice]
SSB -- Log event --> SCB[SCITT Instance Bob]
SCA -- Federation event --> SCB
SCB -- Federation event --> SCA
SCA -- Trigger collaborative actions --> VCSB
SCB -- Trigger collaborative actions --> VCSA
classDef servers fill:#f9f,stroke:#333,stroke-width:2px;
classDef engines fill:#bbf,stroke:#333,stroke-width:2px;
classDef statements fill:#bfb,stroke:#333,stroke-width:2px;
classDef scitt fill:#ff9,stroke:#333,stroke-width:2px;
class VCSA,VCSB servers;
class PA,PB engines;
class SSA,SSB statements;
class SCA,SCB scitt;
Here's sequence diagram depicting webhook event processing, statement generation, and federation interaction:
sequenceDiagram
participant VCSA as VCS Server Alice
participant PA as Policy Engine Alice
participant SSA as SCITT Statement Alice
participant SCA as SCITT Instance Alice
participant SCB as SCITT Instance Bob
participant SSB as SCITT Statement Bob
participant PB as Policy Engine Bob
participant VCSB as VCS Server Bob
VCSA->>PA: Trigger webhook event (e.g., commit)
PA->>SSA: Validate event and create SCITT Statement
SSA->>SCA: Log event to Alice's SCITT Instance
SCA->>SCB: Federate event to Bob's SCITT Instance
SCB->>VCSB: Propagate federated event
VCSB->>PB: Evaluate federated event
PB->>SSB: Validate event and create SCITT Statement
SSB->>SCB: Log event to Bob's SCITT Instance
SCB->>SCA: Federate response event to Alice's SCITT Instance
SCA->>VCSA: Propagate federated response event
In this ecosystem, webhook events from Alice's and Bob's VCS servers initiate CI/CD workflows or repository updates that are validated and captured in SCITT as transparent statements which serve as records for each repo actions, ensuring the integrity of their collaborative efforts. Federation protocols enable sharing these statements across their SCITT instances, allowing the AGIs to maintain a coherent state across poly-repos and collaborate effectively while preserving their operational and system contexts. This setup not only enhances the security posture by ensuring actions are validated and authentic, but also fosters collaboration by maintaining a shared history of contributions across their poly-repo environment.
Alice and Bob represent two advanced software systems with self-hosted version control servers. They need to safely collaborate across multiple software projects while ensuring the actions taken on their codebases are transparent and traceable.
To achieve this, they utilize a combination of modern security and networking protocols—namely, a secure logging mechanism known as SCITT, which is like a ledger to record and verify noteworthy activities, like code updates or bug comments, in a way that cannot be repudiated. Additionally, they use something called webhooks, which are automated messages sent from their version control systems when specific actions occur, such as code being committed.
Alice and Bob each have their own rules and policies that govern how they operate—a sort of strategic playbook. These policies are enforced using what is known as a policy engine, which considers contextual information. Think of it as a highly sophisticated filter that decides what activity is relevant or allowed according to their set of rules.
When Alice's system gets a webhook event, like a code update, it processes this through its policy engine, and if it passes muster, it's recorded in its SCITT ledger as a statement of action, which is like stamping "approved" on a document. Once recorded, this information can be shared with Bob's system through a federated network—a network where both systems share and recognize each other's ledgers. Bob's system undergoes a similar process, and the mutual exchange of "approved" actions allows them to collaborate effectively in their software development efforts.
By using these technologies, Alice and Bob ensure that their collaboration is not only efficient but also adheres strictly to their security requirements and strategic goals. The transparency provided by the SCITT ledger ensures a high degree of accountability and trust in their joint operations.
Draft deck for status update:
- Summary of the seamless, secure collaboration protocol established between AGI instances, Alice and Bob, across multiple repository environments.
> ### Slide 2: Manager's Speak - Executive Summary
- Introduction to the need for secure, transparent collaboration between advanced software systems.
- Overview of SCITT as the transparency ledger for recording and sharing approved activities.
- The role of policy engines in ensuring adherence to each AGI's operational policies.
- The collaboration challenge in a multi-repository setup.
- The necessity for a system that ensures integrity and trustworthiness.
- The federated system that allows sharing trusted actions across different AGIs.
- Explanation of technologies involved:
- Webhooks: Automated notifications for events like commits or comments.
- Policy Engine: Filters events based on a set of predefined rules.
- SCITT (Supply Chain Integrity, Transparency, and Trust): Ledger to log events.
- Federation Protocols: Enable AGIs to share SCITT log entries.
- Detailed view of the event validation process:
- Capture through webhooks.
- Verification against strategic rules and threat models.
- Logging of events in SCITT after validation.
- Re-broadcast to federated instances for synced collaboration.
- Flowchart detailing inter-AGI communication:
- Verification of incoming federated events.
- Reflecting changes across both AGIs for synchronized development.
- The process of transforming webhook events into SCITT statements.
- Presentation of flowcharts showing the validation and federation of events for collaboration.
- Introduction to LPGs and ontologies for managing policies.
- How they ensure context-aware decisions within each AGI system.
- Demonstrating how AGIs integrate event handling with their VCS.
- Sequence diagrams illustrating the handling and federation of webhook events.
- Summary diagram showing the complete interaction chain from webhook capture to SCITT federation.
- The role each technology plays in securing and simplifying collaborative development efforts.
- Recap of benefits provided by the poly-repo management system.
- The strategic impact on AGI development and potential areas for implementation.
Final Notes for Presenter:
While delivering the presentation, start with the broad concerns and executive summary to capture the audience’s attention. Then delve into the specific technologies, illustrating how each component of the system functions and interacts to ensure a robust collaborative development environment. Emphasize the strategic value provided by these processes in promoting transparency and efficiency. Conclude by opening the floor to questions and discussions on potential implementation strategies.
Alice and Bob can use their secure and collaborative AGI-driven environments to bootstrap further infrastructure by leveraging the verified and transparent activities in their poly-repo setup. The infrastructure expansion can include further development environments, production systems, or even extended AGI networks. Here's a high-level overview of how they might achieve this:
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Infrastructure as Code (IaC): Using the reliable history of changes and actions in their VCS, Alice and Bob can create IaC scripts that define and automate the deployment of new environments, ensuring consistency and adherence to established patterns.
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Continuous Integration and Deployment (CI/CD): By incorporating the SCITT statements, they can trace every aspect of CI/CD workflows, assuring that each automated build or deployment follows expected protocols and originates from verified changes.
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Dynamic Environment Creation: With verified CI/CD practices, new environments (development, staging, production) can be spun up dynamically, reflecting the current state of development as logged in the federated SCITT instances.
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Resource Configuration Management: Leveraging the auditable SCITT statements, they can manage configuration changes across multiple platforms. This provides a foundation for synchronizing settings and updates in a decentralized manner while ensuring trust.
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Security Policy Enforcement: Using the policy engines and the SCITT infrastructure, Alice and Bob can enforce security policies and compliance requirements as part of the bootstrapping process. These policies ensure that the newly created infrastructure adheres to the same security standards as existing systems.
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Automated Verification and Consistency: Newly bootstrapped environments can automatically be checked against the SCITT statements and LPG-driven policies to ensure they align with the agreed-upon standards and operational context.
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Collaborative Multi-AGI Operations: As new AGI instances might be part of the expanded infrastructure, these can be integrated into the existing federation, extending the network of trust and allowing for seamless collaboration across a broader AGI ecosystem.
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Monitoring and Auditing: Continuous monitoring systems can be set up following the same principles, where anomalies or non-compliant changes trigger alerts and are logged as SCITT statements for audit purposes.
Detailed steps in the bootstrapping process might look like this:
- Step 1: Evaluate current repository states and CI/CD pipelines using the local SCITT instance data.
- Step 2: Define the necessary infrastructure setups and configurations as code.
- Step 3: Run IaC scripts through the policy engine to ensure compliance and security.
- Step 4: Execute IaC scripts to provision and configure the new infrastructure.
- Step 5: Each action taken by the IaC tooling generates webhook events, which are processed into SCITT statements for an immutable record.
- Step 6: Validate the health and policy adherence of the new infrastructure against SCITT statements.
- Step 7: Integrate new environments into the AGIs' operational processes, such as monitoring and federated event handling.
By using SCITT as a backbone for trust and verification, the AGIs can ensure that the expansion of their infrastructure is not only swift and consistent but also maintains the integrity and security needed for reliable AGI development and operations.
SCITT can be integrated with Kubernetes to provide a high-integrity log of state changes and serve as a policy decision point for admission control. This integration ensures that all changes within the Kubernetes cluster are authorized, logged, and verifiable.
The integration could work as follows:
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Admission Control: A Kubernetes admission controller is deployed that intercepts requests to the Kubernetes API server. Before any creation, update, or deletion of resources within the cluster, the admission controller sends the request details to the policy engine.
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Policy Engine Evaluation: The policy engine, guided by SCITT statements and organizational policies, evaluates the request to ensure it complies with the predefined security standards and operational practices.
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SCITT Logging: Upon successful policy evaluation, a SCITT statement is generated to log the change. The SCITT statement captures the what, who, when, and how of the requested change, offering complete traceability.
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Approval and Logging: If the request passes the policy engine's evaluation, the admission controller allows it, and the action is logged in SCITT as an immutable record. If it's denied, the request is rejected, and the attempted change is also logged for audit purposes.
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State Watcher: A Kubernetes watcher service tracks changes to cluster state and generates SCITT statements that log these changes. These statements serve as a tamper-evident timeline of cluster events.
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State Change Validation: For requests that result in changes, follow-up checks validate that the state of the cluster reflects what was approved, ensuring no unauthorized modifications slipped through.
Here is a mermaid diagram visualizing this process:
sequenceDiagram
participant User as User/API Client
participant AC as Admission Controller
participant PE as Policy Engine
participant SL as SCITT Logger
participant KS as Kubernetes Server
participant KW as Kubernetes Watcher
User->>AC: Request to create/update/delete resource
AC->>PE: Evaluate request against policies
PE-->>AC: Approval/Rejection
AC-->>User: Admission decision
AC->>SL: Generate SCITT statement (if approved)
SL->>SL: Log SCITT statement
AC->>KS: Apply approved changes (if any)
KS->>KW: Notify of state changes
KW->>SL: Generate SCITT statements for state changes
SL->>SL: Log SCITT statement
In this sequence:
- User/API Client sends a resource management request to Kubernetes.
- Admission Controller captures the request and queries the Policy Engine.
- Policy Engine approves or rejects based on current policies and logs the action as a SCITT Statement via the SCITT Logger.
- Kubernetes Server applies the changes if approved.
- Kubernetes Watcher monitors cluster state changes and logs them as SCITT statements.
- SCITT Logger maintains a tamper-evident ledger of statements for auditing.
By integrating SCITT with Kubernetes, Alice and Bob can ensure that the cluster's state always reflects approved and validated states from their CI/CD workflows, maintaining security and consistency across their development operations. This integration also creates an audit trail for all changes, providing complete visibility into cluster events and enabling rapid response to potential policy violations.