Build Secure SOC AI Incident Investigation Agent, Part 1

Mitigate Prompt Injections, Jailbreaks, Data Leaks and Misalignment Issues

AI-driven incident investigation can dramatically reduce mean time to detection (MTTD) and resolution (MTTR). In this two-part series, we introduce a secure, low-code “Investigation Agent” framework that coordinates specialized sub-agents to classify incidents, gather evidence, perform historical context analysis, and assemble a structured report—all in minutes.

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Current Challenges

CISO Challenges

1. High MTTR & MTTD (Mean time to Detect and Respond)

2. Alert Fatigue—too many noisy alerts overwhelm analysts

Security-Tool Challenges

1. Model Hallucinations—incorrect or fabricated outputs

2. Prompt-Injection & Data Leaks—malicious or accidental leakage of sensitive prompts or data

3. Scalability & Cost—large LLM context windows (20K–50K tokens) per incident drive compute costs

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Goal

Securely design AI agents to speed up incident investigation, shrinking analysis from hours to 1–3 minutes while mitigating security risks such as Prompt Injections, Data Poisoning, Hallucinations and Data Leaks.

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Architecture

At the high level “Investigation Agent” orchestrates four specialist sub-agents:

• Investigation Agent: reads history, picks the next sub-agent

• Classifier Agent: fetches incident by ID, assigns Phishing/Malware/Unauthorized Access/Insider Threat/Other

• Evidence Lookup Agent: retrieves playbook tasks run against the incident, extracts key clues

• Historical Analysis Agent: finds prior incidents mentioning core entities (IPs, domains, filenames)

• Report Writer Agent: compiles a Markdown report with executive summary, timeline, steps, evidence, MITRE mapping, recommendations

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Workflow

1. User Request

   • Analyst enters:

investigate 12345

1. Investigation Agent

   • Picks IncidentTypeClassifierAgent.

2. Classifier Agent

   • Fetches incident data and classifies:

{"incident_id":"12345","type":"Phishing"}

1. Evidence Lookup Agent

   • Retrieves executed playbook tasks and summarizes clues:

     Checked IP 203.0.113.5 – low abuse confidence 2 Quarantined suspicious email 3 Extracted attributes from .msg file 4 Condition check failed, missing IOC 5 Incidents status updated to “CLOSED”

2. Historical Analysis Agent

   • Identifies core entities (e.g. IP 203.0.113.5) and finds past occurrences:

     • “IP 203.0.113.5 appeared in two prior phishing incidents (both benign).”

     • “Domain malicious.example.com seen once, linked to credential theft.”

3. Report Writer Agent

   • Produces structured Markdown report:

## INVESTIGATION_12345

**Executive Summary**  
- **Status:** Suspicious  
- **Reason:**  
  - Email quarantined due to high abuse reputation.  
  - Inconsistent condition checks.

**What Happened?**  
- 2025-06-24T10:15Z – Email flagged by gateway.  
- 2025-06-24T10:17Z – IP 203.0.113.5 reputation checked.  
- 2025-06-24T10:18Z – Email quarantined and Incidents closed.

**Investigation Steps**  
1. Classified incident type.  
2. Retrieved and summarized playbook tasks.  
3. Queried historical occurrences of key entities.

**Evidences**  
- Quarantine log entry  
- IP reputation report  
- Condition-check failure details

**MITRE Mapping**  
- **T1566 (Phishing):** Email quarantine triggered.

**Recommendations**  
- Block 203.0.113.5 at the firewall.  
- Enforce SPF for external senders.

Benefits

• Economical: handle 20K–50K tokens per incident

• Rapid Response: full investigation in 1–3 minutes

• Low-Code Deployment: easily configure and run agents without extensive coding

Key Technical Challenges

• Cost of Handling Every Alert with an LLM

  • Running full-context LLM workflows on each alert is prohibitively expensive. We address this with metalearning agents that adaptively choose lightweight preprocessing for routine checks.

• Validation of Agents

  • Ensuring each sub-agent’s outputs remain accurate over time requires rigorous automated testing and concrete validation datasets.

• Agent Drift

  • Model or prompt changes can cause shifts in behavior (“drift”). Continuous monitoring and periodic retraining of agents are essential.

• Security Challenges

  • Beyond prompt injection and data leaks, sandboxing, strict access controls, and audit logging are necessary to prevent misuse.

Security Risks

• Direct Prompt Injection: crafted inputs that manipulate prompts

• Indirect Prompt Injection: malicious data in tool outputs

• Data Leaks: exposing sensitive incident details in LLM context

Stay tuned for Part 2: “Securing Your Incident Investigation Agent”—we’ll dive deep into sandboxing, prompt hardening, access controls, and logging.