SMAP Unleashed: How Seqrite’s AI-Powered Malware Analysis Platform Is Redefining Threat Intelligence And Incident Response + Video

Introduction:

Every malware sample tells a story—the challenge lies in uncovering its narrative before attackers can weaponize it. Modern Security Operations Centers (SOCs) are drowning in alerts, yet traditional signature-based detection routinely fails against polymorphic threats, zero-day exploits, and fileless malware that leaves no disk footprint. Seqrite’s Malware Analysis Platform (SMAP), enhanced by the proprietary GoDeep.AI engine, addresses this critical gap by shifting the paradigm from reactive detection to proactive, intelligence-driven analysis—transforming how security teams investigate, correlate, and neutralize sophisticated cyber threats across hybrid enterprise environments.

Learning Objectives:

Master the multi-stage malware analysis workflow—from preliminary ML-based triage to dynamic sandbox execution and manual reverse engineering.
Operationalize threat intelligence by extracting Indicators of Compromise (IOCs) and mapping adversary Tactics, Techniques, and Procedures (TTPs) to the MITRE ATT&CK framework.
Integrate SMAP’s API with existing SIEM, SOAR, and EDR/XDR ecosystems to automate incident response and reduce Mean Time to Respond (MTTR).

Multi-Stage Analysis Pipeline: From Static Triage to Manual Reverse Engineering

SMAP’s architecture is built on a layered analysis approach that systematically deconstructs every suspicious file. The process begins with Preliminary Analysis, where machine learning models and Seqrite’s rich telemetry—drawing from a repository of over 2 billion classified files and processing roughly 1 million new samples daily—perform a reputation check. This stage inspects code structure, metadata, and file properties without executing the sample, quickly filtering known clean or malicious files.

If the preliminary scan raises red flags, the sample moves to Dynamic Analysis, where it is executed in an isolated, secure sandbox environment. Here, real-time behavioral monitoring captures network connections, file system modifications, registry changes, and process injections. The sandbox also analyzes network traffic to detect command-and-control (C2) communications, botnet activity, and data exfiltration attempts.

For the most evasive and complex threats, Manual Analysis allows threat researchers to reverse-engineer the malware, decode obfuscated routines, and understand the full scope of its functionality.

Step‑by‑step guide for submitting and analyzing a file via SMAP (Web UI):

Access the Platform: Navigate to your SMAP instance (on-premise or cloud) and log in with your credentials.
Upload the Sample: Click on the “Analyze File” tab. Drag and drop or browse to select the suspicious file. SMAP supports a wide range of file types, including executables, documents, PDFs, scripts, and archives.
Initiate Analysis: Click “Upload” . The platform automatically begins the multi-stage pipeline. You can monitor the progress in the “Upload History” section.
Review the Report: Once complete, open the Analysis Report. It provides:

– Summary: Overall verdict (malicious, clean, suspicious).
– Static Attributes: File hashes (MD5, SHA1, SHA256), file type, compiler details, and import/export tables.
– Detonation Detail: Behavioral logs, network connections, process tree, and screenshots of the execution.
– IOC & TTP Mapping: Extracted indicators and mapped adversary techniques aligned with the MITRE ATT&CK framework.
5. Export Results: Download the report in PDF or JSON format for sharing, auditing, or ingestion into other tools.

Relevant Linux/Windows Commands for Local Triage (Before Submission):

Before uploading a file to SMAP, security analysts often perform local triage using built-in OS tools:

Linux – Calculate File Hashes (for SMAP Advanced Search):
```
md5sum suspicious_file.exe
sha1sum suspicious_file.exe
sha256sum suspicious_file.exe
```
These hashes can be used in SMAP’s Advanced Search to check if the file has been previously analyzed.
Linux – Extract Strings and Check for Obfuscation:
```
strings suspicious_file.exe | grep -i "http|https|cmd|powershell"
```
This quickly reveals embedded URLs, IP addresses, or suspicious command-line invocations.
Windows – Check File Metadata and Digital Signatures:
```
Get-Item -Path "C:\Path\to\suspicious_file.exe" | Format-List 
Get-AuthenticodeSignature -FilePath "C:\Path\to\suspicious_file.exe"
```
Verifying the digital signature helps determine if the file is from a legitimate publisher or has been tampered with.
Windows – Monitor Process Creation (for behavioral context):
```
Get-WmiObject Win32_Process | Select-Object ProcessName, CommandLine
```
This can help identify suspicious child processes spawned by the sample before sandboxing.

2. Harnessing GoDeep.AI: The Self-Aware Malware Hunting Engine

At the heart of SMAP lies GoDeep.AI, Seqrite’s proprietary artificial intelligence engine that continuously learns from new threats and refines its detection models. Unlike traditional signature-based antivirus, which relies on known patterns, GoDeep.AI focuses on behavioral patterns and attack methodologies rather than just file appearances. It analyzes system activity, application behavior, and execution patterns to identify suspicious actions even when the specific threat has never been seen before.

GoDeep.AI operates as a unified intelligence framework across Seqrite’s entire security ecosystem, meaning that improvements made in one area (e.g., endpoint protection) instantly enhance detection across all other products, including SMAP. This centralized, continuous learning approach ensures that the platform evolves in lockstep with the rapidly changing threat landscape.

Step‑by‑step guide to leveraging GoDeep.AI’s intelligence within SMAP:

Submit a Suspicious URL or IP: Use SMAP’s URL Lookup feature. Paste a suspicious URL or IP address into the search bar.
Initiate Reputation Check: The platform queries GoDeep.AI’s vast threat intelligence database, which includes over one billion classified records.
Analyze the Verdict: The response provides a reputation score, category (e.g., phishing, malware distribution, botnet C2), and any associated threat intelligence context.
Correlate with File Analysis: If the URL is linked to a malware sample you are analyzing, SMAP can correlate the network indicators from the dynamic analysis with the URL reputation, providing a complete picture of the attack chain.

API Integration for Automated Threat Intelligence Enrichment:

SMAP’s RESTful API allows seamless integration with SIEM, SOAR, and EDR/XDR platforms. Here’s a practical example of using `curl` to submit a file for analysis and retrieve results programmatically:

Submit a file for analysis (Linux/macOS):

curl -X POST "https://your-smap-instance.com/api/v1/analyze" \
-H "Authorization: Bearer YOUR_API_KEY" \
-F "file=@/path/to/suspicious_file.exe" \
-F "priority=high" \
-F "tags=phishing,campaign_2026"

Check analysis status:

curl -X GET "https://your-smap-instance.com/api/v1/analysis/{analysis_id}/status" \
-H "Authorization: Bearer YOUR_API_KEY"

Retrieve the full report in JSON (for SIEM/SOAR ingestion):

curl -X GET "https://your-smap-instance.com/api/v1/analysis/{analysis_id}/report" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Accept: application/json" > threat_report.json

This automation enables SOC teams to build playbooks that automatically submit suspicious files from email gateways or EDR alerts, drastically reducing manual triage time.

Extracting Actionable Intelligence: IOCs, TTPs, and MITRE ATT&CK Mapping

The true value of malware analysis lies not just in detecting a threat but in generating actionable intelligence that can be used for threat hunting, incident response, and proactive defense. SMAP automatically extracts Indicators of Compromise (IOCs) —including file hashes, IP addresses, domain names, registry keys, and mutexes—from both static and dynamic analysis. More critically, it maps attacker behavior to the MITRE ATT&CK framework, categorizing observed actions into tactics (e.g., Execution, Persistence, Privilege Escalation) and techniques (e.g., T1059 Command and Scripting Interpreter, T1547 Boot or Logon Autostart Execution).

Step‑by‑step guide for operationalizing IOCs and TTPs:

Access the Analysis Report: After a file analysis completes, navigate to the Analysis Report section.
Review the IOC Tab: This section lists all extracted indicators, often with severity ratings (high, medium, low). Export the list in STIX or OpenIOC format for sharing with threat intelligence platforms.
Examine the MITRE ATT&CK Mapping: Review the mapped techniques. This helps understand the adversary’s objectives and potential next steps.

4. Deploy IOCs to Defensive Controls:

Firewall/IPS: Block the extracted malicious IPs and domains.
EDR: Create custom detection rules for the observed process behaviors and registry modifications.
SIEM: Develop correlation rules to hunt for similar TTPs across your enterprise.

Update Threat Intelligence Feeds: Share the enriched intelligence with your threat sharing community (e.g., ISACs) to help protect the broader ecosystem.

Practical Command Examples for IOC Deployment:

Linux – Block Malicious IPs using iptables:

sudo iptables -A INPUT -s 192.168.1.100 -j DROP
sudo iptables -A OUTPUT -d 192.168.1.100 -j DROP

Replace `192.168.1.100` with the extracted C2 IP address.

Windows – Add Malicious Domains to the Hosts File (for containment):
```
Add-Content -Path "C:\Windows\System32\drivers\etc\hosts" -Value "127.0.0.1 malicious-domain.com"
```
This effectively sinks the domain, preventing any host from resolving it.

Windows – Query Sysmon Logs for Specific Process Creation (Threat Hunting):

Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; ID=1} | Where-Object { $<em>.Message -match "powershell.exe" -and $</em>.Message -match "-EncodedCommand" }

This hunts for encoded PowerShell commands, a common TTP (T1059.001) used by attackers.

4. Sandbox Evasion Detection and Secure Execution Environment

Modern malware frequently employs sandbox evasion techniques—such as long sleep timers, environment checks, or conditional execution based on the presence of virtual machine artifacts—to avoid detection during dynamic analysis. SMAP’s sandbox is designed to counter these evasive tactics through several mechanisms:

Extended Execution Timeouts: The sandbox monitors samples for extended periods, triggering on delayed malicious activity.
User Interaction Simulation: It simulates mouse clicks, keyboard inputs, and system reboots to trigger malware that requires user interaction.
Network Traffic Analysis: Even if the malware attempts to communicate over non-standard ports or uses encryption, SMAP analyzes network behavior patterns to detect anomalies.

Step‑by‑step guide for analyzing evasive malware in SMAP:

Submit the Sample: Upload the file as described in Section 1.
Enable Advanced Sandbox Settings (if available): Some deployments allow analysts to specify execution parameters, such as operating system version, user privileges, or network simulation.
Monitor Detonation Logs: Pay close attention to the Detonation Detail section. Look for:

– Sleep Calls: If the malware used `Sleep()` or similar API calls, the report will show the delay and whether the sandbox overrode it.
– Environment Checks: Check if the malware attempted to query system information (e.g., GetSystemInfo, WMI queries) to detect virtualized environments.
4. Review Network Traffic: Examine the Network Traffic Analysis section for any attempted C2 communications, even if they were blocked or failed.
5. Supplement with Manual Analysis: If the sandbox fails to trigger malicious behavior, threat researchers can perform Manual Analysis by reversing the binary to understand its evasion logic and develop custom YARA rules for detection.

Seamless Integration with Security Ecosystems: SIEM, SOAR, and EDR/XDR

SMAP is not an isolated tool; it is designed to be a force multiplier within your existing security stack. Its SMAP API ensures seamless integration with leading SIEM, SOAR, and EDR/XDR platforms. This integration enables automated sample submissions, real-time threat intelligence enrichment, and orchestrated incident response workflows.

Step‑by‑step guide for integrating SMAP with a SOAR platform (e.g., Palo Alto Cortex XSOAR):

Obtain API Credentials: Generate an API key from the SMAP administration console.
Configure the Integration in SOAR: In your SOAR platform, create a new integration instance for SMAP. Provide the API endpoint URL and the API key.
Create an Automation Playbook: Build a playbook that triggers when a high-severity alert (e.g., from EDR) is received.

– Step 1: Extract the file hash from the alert.
– Step 2: Query SMAP to check if the hash has been previously analyzed (using the Advanced Search API).
– Step 3: If not found, submit the file for analysis via the SMAP API.
– Step 4: Poll for analysis completion and retrieve the report.
– Step 5: Parse the report and enrich the original alert with IOCs, TTPs, and the MITRE ATT&CK mapping.
– Step 6: If the verdict is malicious, automatically create a ticket in your IT service management system and block the IOCs on your firewall or EDR.

Example API Call for Advanced Search (by SHA-256):

curl -X GET "https://your-smap-instance.com/api/v1/search?hash=sha256:4e8f5e...（hash）" \
-H "Authorization: Bearer YOUR_API_KEY"

This level of automation dramatically reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR), allowing security teams to focus on high-value investigative work rather than repetitive triage.

6. Compliance, Forensics, and Audit Readiness

For regulated sectors such as finance, healthcare, and government, SMAP provides essential capabilities for breach investigation, evidence storage, and audit support. The detailed analysis reports, which are available in both PDF and JSON formats, serve as forensic evidence that can be presented to regulators or legal counsel. The platform also maintains a comprehensive audit trail of all user activities, ensuring accountability and compliance with standards like GDPR, HIPAA, and PCI-DSS.

Step‑by‑step guide for forensic investigation using SMAP:

Collect the Suspicious File: Obtain the file from the compromised endpoint or network capture.
Submit for Full Analysis: Upload the file to SMAP and ensure both static and dynamic analysis are performed.
Preserve the Report: Download the JSON report for long-term storage in your forensic case management system. The JSON format is machine-readable and can be ingested into analysis tools.
Extract Timeline of Events: Use the process tree and timeline from the Detonation Detail section to reconstruct the attack sequence.
Map to MITRE ATT&CK: Use the mapping to articulate the adversary’s tactics and techniques in your incident report.
Export IOCs for Threat Hunting: Use the extracted IOCs to hunt for the same threat across your entire enterprise, identifying any other compromised hosts.

What Undercode Say:

Key Takeaway 1: SMAP’s multi-stage analysis pipeline—combining ML-based triage, dynamic sandbox execution, and manual reverse engineering—provides a comprehensive framework that addresses the entire malware analysis lifecycle, from initial detection to deep forensic investigation. This layered approach ensures that even the most evasive threats are eventually uncovered and understood.
Key Takeaway 2: The integration of GoDeep.AI’s behavioral AI engine and the platform’s seamless API connectivity transforms SMAP from a standalone analysis tool into a central intelligence hub within the modern SOC. By automating IOC extraction, MITRE ATT&CK mapping, and integration with SIEM/SOAR workflows, SMAP enables security teams to move from reactive alert-chasing to proactive, intelligence-driven threat hunting.

Analysis: The cybersecurity industry is witnessing a fundamental shift from signature-based detection to behavioral and AI-driven analysis. Seqrite’s SMAP, powered by GoDeep.AI, exemplifies this transition by offering a unified platform that not only detects known malware but also uncovers novel threats through behavioral analysis. The platform’s ability to process over 1 million samples daily and maintain a repository of 2 billion classified files gives it a significant advantage in threat intelligence richness. However, the true differentiator lies in its automation capabilities—by providing a robust API and pre-built integrations, SMAP allows organizations to embed advanced malware analysis directly into their incident response workflows, drastically reducing the time from detection to remediation. For SOC teams struggling with alert fatigue and staffing shortages, this level of automation is not just a luxury but a necessity.

Prediction:

+1 The integration of AI-driven behavioral analysis into malware analysis platforms like SMAP will become the industry standard within the next 24 months, rendering traditional signature-based antivirus obsolete for enterprise security.
+1 Automated IOC extraction and MITRE ATT&CK mapping will increasingly be embedded into SOAR playbooks, enabling fully autonomous threat containment and eradication cycles without human intervention for low-to-medium severity incidents.
-1 As AI-powered malware analysis becomes more accessible, adversaries will respond by developing more sophisticated evasion techniques specifically designed to defeat behavioral AI engines, including adversarial machine learning attacks that poison training data or generate evasive malware variants.
-1 The reliance on centralized threat intelligence repositories, while powerful, creates a single point of failure—if an attacker can compromise or manipulate the intelligence feed, they could potentially blind a large number of organizations simultaneously.

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