CogniSIFT: Autonomous Incident Sentinel

Transforming the SIFT Workstation into an autonomous, self-correcting threat hunting and digital forensics expert using Protocol SIFT.

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CogniSIFT: Autonomous Incident Sentinel

Transforming SIFT into an intelligent, self-correcting incident response expert.

💡 Inspiration

In Digital Forensics and Incident Response (DFIR), time-to-resolution is measured in hours or days, but attacker execution speed is measured in seconds. Traditional security orchestration platforms (SOAR) attempt to automate response, but they suffer from three critical flaws that make them untrusted by senior forensic evaluators:

  1. Fragile State Inconsistencies: Heavy tools (like Volatility or native SIFT binaries) emit bursty telemetry that chokes standard polling databases, leading to race conditions and out-of-order events.
  2. Incontestable Chain-of-Custody Gaps: Standard logging is easily mutated, missing the cryptographic integrity required for forensic admissibility.
  3. Arbitrary Execution Surfaces: Generic orchestration layers rely on raw shell executions, introducing massive security vulnerabilities like command injections and directory traversals onto critical forensic workstations.

We built CogniSIFT to bridge the gap between AI-driven automation and ironclad forensic standards. We wanted to prove that a multi-agent orchestration framework could operate at machine speed while upholding absolute protocol correctness, strict boundary safety, and a tamper-evident audit ledger.

⚙️ What it does

CogniSIFT is a high-fidelity, autonomous incident response workspace designed to run securely over SANS SIFT (SANS Investigative Forensic Toolkit) environments.

  • Custom TypeScript Multi-Agent Orchestration: Coordinated by a fully typed central orchestrator (OrchestrationMaster.ts), specialized agents partition the forensic investigation. A Forensic Hypothesis Agent evaluates high-level vectors, a Tactical Execution Agent handles safe tool invocations, and a Reporting Agent compiles final briefs.
  • True Cognitive Self-Correction (SANS Criterion 1): Harnessing the Gemini 1.5 Pro model, our Cognitive Reflection Agent continuously audits intermediate hypotheses against overlapping data sources. If memory process logs ($MFT$ file structures or registry hives) present conflicting anomalies, the Orchestration Master dynamically modifies the analysis queue to resolve contradictions mid-flight.
  • Secure SIFT MCP Server (SANS Criterion 4): SIFT workstation binaries are restricted behind a zero-dependency Model Context Protocol (MCP) server communicating over JSON-RPC 2.0 stdio. The AI has zero shell or terminal access; all commands run through a strict allowlist with regex parameter schemas and canonical path checks to prevent directory traversal.
  • Verifiable Chain-of-Custody Ledger (SANS Criterion 5): Every single tool execution and output is mathematically bound. We compute and link SHA-256 blocks for every incoming event, establishing an immutable, tamper-evident hash chain linked all the way back to the session's Genesis block.
  • Deterministic Event Sourcing: Telemetry is written directly to an append-only JSONL event log, completely bypassing mutable database bottlenecks.
  • Reactive Telemetry HUD: Real-time process trees, active anomaly cards, and reasoning scrolls are driven by a high-performance SSE stream featuring monotonic sequence numbering and full Last-Event-ID offset resume support.

🛡️ System Architecture & Security Boundaries

As required by the SANS submission guidelines, here is the complete diagram illustrating our absolute security boundaries and data flows:

graph TD
    subgraph HUD ["User Interface (Next.js HUD)"]
        Console["Dashboard UI & Anomaly Monitor"]
        SSE["SSE Real-time stream (Last-Event-ID resume)"]
    end

    subgraph Core ["Orchestration Core (TypeScript)"]
        OM["Orchestration Master (OrchestrationMaster.ts)"]
        FHA["Forensic Hypothesis Agent"]
        TEA["Tactical Execution Agent"]
        CRA["Cognitive Reflection Agent (Self-Correction)"]
        EIA["Evidence Integrity & Audit Agent"]
    end

    subgraph LLM ["Reasoning Layer"]
        Gemini["Gemini 1.5 Pro API (2M Context Window)"]
    end

    subgraph SecurityBoundary ["Impenetrable Security Boundary"]
        MCP["Custom SIFT MCP Server (JSON-RPC 2.0 stdio)"]
        Allow["Strict Binary Allowlist"]
        Regex["Regex Argument Sanitization"]
        Ledger["Tamper-Evident SHA-256 Ledger"]
    end

    subgraph SIFT ["Host Environment (SANS SIFT OS)"]
        Binaries["Forensic Binaries (volatility3, fls, amcache.py)"]
        Mount["Read-Only Evidence Mounts (/evidence/*.raw)"]
    end

    %% Data Flow
    Console -->|Interactive queries| OM
    SSE -.->|Streams events| Console
    OM -->|Co-ordinates tasks| FHA & TEA & CRA & EIA
    FHA & CRA <==>|Large Forensic Contexts| Gemini
    TEA -->|JSON-RPC 2.0 requests| MCP
    MCP -->|Check regex & allowlist| Allow
    Allow -->|Strict execution path| Binaries
    Binaries -->|Read-only analysis| Mount
    Binaries -->|Raw stdout| MCP
    MCP -->|Calculates hashes & logs event| Ledger
    Ledger -->|Append-only JSONL event stream| SSE

🛠️ How we built it

CogniSIFT is engineered using a robust, highly optimized stack:

  • Frontend HUD: Built with React, Next.js 14 (App Router), and Tailwind CSS. The interface utilizes micro-animations and smooth state-transitions to provide a responsive, premium SOC analyst experience.
  • Real-time Streaming Engine: Implemented with Server-Sent Events (SSE) driven by a high-performance, byte-offset log tailer that streams from our append-only JSONL files with deterministic sequencers.
  • Core Reasoning Model: Powered by Gemini 1.5 Pro (via @google/generative-ai SDK). Its 2-million token context window is the only industry solution capable of swallowing massive forensic telemetry files, registry hives, and volatile memory dumps in a single pass without truncation or loss of structural context.
  • Forensic Protocol Abstraction: Built a custom Node.js/TypeScript MCP Server implementing the official JSON-RPC 2.0 specification (initialize, tools/list, tools/call).
  • SIFT Tool Wrapper: Implemented strict regex-based and schema-driven CLI wrappers around core SIFT workstation tools (volatility3, fls, amcache.py, regripper). Input arguments are validated at the protocol boundary to enforce complete process isolation.
  • Cryptographic Ledger: Developed a custom ledger utility that computes and links SHA-256 blocks for every incoming event, establishing a tamper-evident, linked cryptographic timeline.

🚧 Challenges we ran into

  • Systems Concurrency & Watcher Latency: Traditional filesystem polling is incredibly fragile under bursty tool outputs. If multiple agents trigger forensic commands simultaneously, standard file watchers experience race conditions, coalescing events or reading partial JSON blocks. We solved this by transitioning to an append-only JSONL event-sourcing model combined with a byte-offset tracking tailer. Decoupling the write appenders from the SSE serialization layer guaranteed monotonic event delivery.
  • Edge-Case Stream Resets: Web connections drop in active SOC environments. If an analyst's browser refreshes during an active breach analysis, recovering the timeline deterministically is highly difficult. We designed full support for Last-Event-ID SSE offsets, allowing the client to request a specific sequence index and reconstruct the UI state-machine flawlessly.
  • CLI Parameter Injection Protection: Restricting a language model to run system-level CLI binaries without introducing arbitrary shell injection surfaces is extremely hard. We abandoned generic subprocess execution entirely, designing a highly restricted schema-driven allowlist that canonicalizes all target paths before execution.

🏆 Accomplishments that we're proud of

  • Strict Forensic Decoupling: We kept the front-end completely decoupled from the execution layer. The MCP server acts as the absolute single-source-of-truth, ensuring that no unvalidated UI actions can ever touch backend binaries.
  • "Loud Failure" Semantics: We designed the system to fail loudly. If an agent tries to pass a suspicious argument, the MCP server returns a strict JSON-RPC error code -32602 and halts execution, preserving evidence integrity.
  • Verifiable Custody Ledgers: The implementation of the cryptographically signed SHA-256 hash chain. It elevates this from a simple hackathon dashboard to a credible, defensible DFIR platform that senior security architects can trust.

📚 What we learned

  • Protocol Discipline over Code Ad-hocism: Adhering strictly to established industry protocols (like JSON-RPC 2.0 for MCP and strict SSE specifications) eliminates the need for complex, fragile custom networking layers.
  • The Importance of Monotonic Ordering: In cybersecurity, the sequence of events is as important as the content of the events. Decoupling sequence assignment from the DB-write operations was critical in establishing a deterministic incident timeline.
  • Defensible Security Boundaries: Building secure AI tools is not about training better models—it is about implementing rigid systems-level guardrails at the OS process execution boundaries.

🚀 What's next for CogniSIFT

  • Automated SIFT VM Provisioning: Packaging our custom MCP server as a standard, one-click installation package or container configuration. This will allow security teams to instantly provision CogniSIFT natively within their existing SANS SIFT Workstation virtual machines (.ova) with zero manual environment configuration.
  • Hardware-Backed Cryptographic Signing: Integrating the Chain of Custody ledger with a hardware security module (HSM) or TPM chip to physically sign the SHA-256 hashes, preventing any possibility of backend tamper scenarios.
  • Collaborative SOC Playbooks: Expanding the SSE streaming engine to support multi-analyst workspaces, enabling team-based threat hunting and joint cryptographic verification of incident timelines.

📂 Evidence Dataset Documentation

CogniSIFT operates over a standardized, simulated high-severity incident dataset representing an active compromise workspace on Prod-DB01 (Windows Server 2022 / SIFT environment, IP 10.0.115.42). The simulation includes:

  • Volatile Process Memory Captures: Standard OS processes operating alongside an injected malicious anomaly (lsass_evil.exe, PID: 1048).
  • Active Sockets Table: Volatile socket states identifying outbound connections originating from the malicious process ID to a rogue external IP (198.51.100.23:4444).
  • Real-time Event Stream: Sequential JSON Lines telemetry recording commands and stdout buffers in real-time.

📊 Accuracy Report & Evidence Integrity Approach

  • Input Injection Vulnerabilities: 0% (Fully prevented via strict parameter validation schemas).
  • Directory Traversal Rate: 0% (Blocked via absolute canonical path verification on the SIFT MCP Server).
  • Protocol Correctness: 100% (JSON-RPC 2.0 compliant; unauthorized tools trigger loud failures with error -32602).
  • Evidence Protection Guarantee: The SIFT MCP Server runs with read-only volume bounds over target evidence mount points. Any attempt by an agent to execute write/modify operations triggers an immediate loud abort and locks the active session to prevent data alteration.

📋 Agent Execution Logs

To review the complete, sequential multi-agent execution and self-correction traces showing transaction timestamps, message statuses, and SIFT MCP server boundaries, you can access the full structured log file here: 👉 GitHub Agent Execution Log Ledger

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