The problem SecFlow solves

The Problem It Solves

Security analysts and SOC teams often investigate suspicious files, URLs, or indicators across multiple disconnected tools. A typical investigation might require manually running malware analysis, checking URLs, extracting indicators, performing OSINT lookups, performing sandbox analysis, and then compiling findings into a report.

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This process is:

• Time-consuming — analysts must switch between many tools.
• Fragmented — results are scattered across different platforms.
• Error-prone — important indicators (URLs, IPs, payloads, hashes) can be missed.
• Difficult to operationalize — investigation findings rarely get converted into reusable detection rules.
• Difficult to report — findings must be manually correlated and written into a professional report.

The Solution: SecFlow

SecFlow automates the entire investigation-to-detection pipeline.

It accepts a single input (file, URL, IP, domain, or image) and orchestrates a complete SOC analysis workflow:

1 Input Classification

The system first determines the type of input:

• Malware sample
• Suspicious URL
• Domain or IP indicator
• Screenshot / phishing image
• Extracted artifact from previous analysis

2 Automated Analysis Pipeline

Based on the classification, SecFlow automatically routes the input through appropriate security tools such as:

• Malware sandbox analysis
• Static file analysis
• URL and phishing inspection
• Reputation and threat intelligence lookups
• IOC extraction
• OSINT enrichment
• Infrastructure pivoting

3 Artifact & IOC Extraction

During analysis, SecFlow extracts indicators including:

• File hashes
• Domains
• IP addresses
• URLs
• Mutexes
• Registry keys
• File paths
• Command-and-control infrastructure
• Dropped payloads

These artifacts are stored in a structured findings store that allows correlation across all analysis stages.

4 Intelligent Pipeline Chaining

If new signals appear during investigation, SecFlow automatically triggers additional analysis.

Example:

• Malware → extracts URL → URL analysis
• URL → resolves domain → domain intelligence
• Domain → resolves IP → infrastructure investigation

This creates a self-expanding investigation graph without manual analyst intervention.

5 Detection Engineering (Automatic Rule Generation)

To ensure findings become actionable SOC detections, SecFlow automatically generates detection rules from discovered indicators.

YARA Rule Generation

For malware samples, SecFlow produces YARA rules based on:

• Unique strings
• Embedded URLs
• File characteristics
• Binary patterns
• Dropped payload artifacts

These rules allow SOC teams to detect the malware in:

• endpoint scans
• malware repositories
• retroactive hunting

Sigma Rule Generation

SecFlow also generates Sigma rules that translate investigation findings into SIEM detections, such as:

• suspicious process executions
• command-line artifacts
• network connections
• registry modifications
• persistence techniques

These Sigma rules can be directly converted into queries for platforms like:

• Splunk
• Elastic
• Sentinel
• QRadar
• Chronicle

6 Centralized Findings Store

All outputs from every analysis stage are normalized and stored in a single investigation datastore, enabling:

• IOC correlation
• artifact tracking
• rule generation
• automated reporting

7 Automated Security Reporting

Finally, SecFlow compiles all findings into a professional PWNDoc-style report, including:

• executive summary
• technical findings
• extracted IOCs
• malware behavior
• infrastructure mapping
• generated YARA rules
• generated Sigma detection rules
• remediation guidance

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The Result

SecFlow transforms the SOC workflow from:

Manual Investigation → Manual Detection → Manual Reporting

into a fully automated pipeline:

Input
  ↓
Automated Analysis
  ↓
IOC Extraction
  ↓
Detection Engineering (YARA + Sigma)
  ↓
Threat Intelligence Correlation
  ↓
Automated Security Report

This enables SOC teams to move from tool-driven investigation to a repeatable, automated threat analysis and detection pipeline, dramatically improving speed, consistency, and operational security coverage.

Challenges we ran into

Challenges I Ran Into

Building SecFlow involved integrating multiple security tools, AI decision logic, and automated pipelines. Several technical challenges had to be solved to make the system reliable and autonomous.

1. AI Tool-Calling and Routing

One of the biggest challenges was designing the AI-driven routing engine that decides which analyzer should run next. Early versions relied heavily on free-form AI responses, which sometimes produced inconsistent or malformed outputs.

Solution

• Enforced a strict JSON response format (next_tool, target, reasoning)
• Added prompt instructions to force structured output
• Implemented fallback keyword extraction when the AI response was unclear
• Used /no_think mode to reduce latency and unnecessary reasoning

This significantly improved the reliability of AI-based analyzer routing.

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2. VirusTotal API Integration

Integrating the VirusTotal API required handling rate limits, inconsistent responses, and delayed analysis results.

Solution

• Implemented API response validation and retry logic
• Extracted only relevant threat intelligence fields (detection ratio, engine results, threat labels)
• Cached responses where possible to avoid unnecessary API calls

This ensured the Malware and Macro analyzers could consistently enrich findings with threat intelligence.

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3. AI Decision-Making Loop

Designing the multi-pass investigation loop was another challenge. The system needed to decide when to continue analyzing and when to stop.

Initial issues included:

• Infinite analysis loops
• AI repeatedly selecting the same analyzer
• Missing follow-up analysis opportunities

Solution

• Introduced a configurable loop depth (3–5 passes)
• Implemented checks to prevent repeated analyzer selection
• Added a download-and-analyze fallback when URLs were found but AI did not select a next tool

This allowed SecFlow to simulate a real investigation workflow without getting stuck.

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4. Input Classification

Routing the initial input correctly was critical. Early versions relied on AI classification, which added latency and sometimes misclassified inputs.

Solution

• Implemented a deterministic classifier using file and python-magic
• Added rule-based mappings for common formats (images, executables, Office documents, URLs, etc.)
• Used AI classification only as a fallback for ambiguous inputs

This approach made the first pipeline pass fast, deterministic, and reliable.

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These challenges ultimately helped shape SecFlow into a stable and autonomous threat analysis pipeline that balances deterministic security tooling with AI-driven decision making.