# Performing Deception Technology Deployment

> Use when deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates. Use when SOC teams need early warning of lateral movement, credential abuse, or internal reconnaissance by deploying convincing traps across the network.

- Skill: `oyi77/performing-deception-technology-deployment` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/performing-deception-technology-deployment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/performing-deception-technology-deployment/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/performing-deception-technology-deployment

---


# Performing Deception Technology Deployment

## Overview

Cybersecurity skill for performing deception technology deployment. Follows industry best practices and security standards.

## When to Use

**Trigger phrases:**
- "performing deception technology deployment"
- "SOC teams need high-fidelity detection of post-compromise lateral movement with"
- "Existing detection tools miss advanced attackers who avoid triggering threshold-"
- "The organization wants to detect credential abuse by planting fake credentials a"


Use this skill when:
- SOC teams need high-fidelity detection of post-compromise lateral movement with near-zero false positives
- Existing detection tools miss advanced attackers who avoid triggering threshold-based alerts
- The organization wants to detect credential abuse by planting fake credentials as honeytokens
- Network segmentation gaps need compensating detection controls

**Do not use** as a replacement for fundamental security controls (patching, EDR, network segmentation) — deception is a detection layer, not a prevention mechanism.


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## Prerequisites

- Network segments identified for honeypot/decoy deployment (server VLANs, DMZ, OT networks)
- Deception platform (Thinkst Canary, Attivo/SentinelOne Hologram, or open-source alternatives)
- SIEM integration for deception alerts (any interaction with deception assets is suspicious)
- Active Directory access for honeytoken account and credential creation
- Network team coordination for IP allocation and traffic routing

## Workflow

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Plan Operations** — Define objectives, scope, and success criteria for deception technology deployment operations.
2. **Prepare Environment** — Set up tools, access, and data sources required for deception technology deployment.
3. **Execute Core Workflow** — Perform the deception technology deployment operations following established procedures.
4. **Validate Results** — Verify that results meet quality standards and objectives.
5. **Report Findings** — Document results, observations, and recommendations.
6. **Follow Up** — Track remediation actions and verify fixes where applicable.

## Tools

- **Analysis Platform** — Data processing and visualization
- **Collaboration Tools** — Team coordination and knowledge sharing


## Process

1. **Design** — Define interface, identify patterns, plan implementation
1. **Implement** — Write code following existing conventions, add tests
1. **Verify** — Run tests, check integration, validate behavior

## Verification

- [ ] All deception technology deployment procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |