How to Protect Against AI-Driven Security Incidents Before 2028

As organizations race to adopt Generative AI, security teams are facing a new reality: the very tools meant to boost productivity are becoming a primary vector for security incidents. According to a recent report by Gartner, by 2028, AI-related issues will drive half of all incident response (IR) efforts.

For defenders, this isn't just a future problem—it is happening now. The rapid deployment of AI models, often without oversight (known as "Shadow AI"), introduces massive risks ranging from data leakage and model poisoning to malicious prompt injections. To defend your organization, you must shift from merely reacting to AI incidents to proactively monitoring AI usage and securing the AI lifecycle.

The Security Issue: Shadow AI and Data Exposure

The core issue driving Gartner’s prediction is the lack of security governance in AI adoption. Business units are eager to leverage Large Language Models (LLMs) to automate workflows, often feeding sensitive intellectual property (IP), customer data, or source code into public models.

When employees paste confidential data into public AI interfaces, that data effectively leaves the organization's perimeter. Furthermore, adversaries are beginning to use AI to accelerate their attacks, creating a dual-threat landscape:

1. Insider Risk via AI: Unintentional data exfiltration through unsanctioned AI tools.
2. Adversarial AI: Attackers using AI to generate more convincing phishing campaigns or discovering vulnerabilities in your own AI models.

Technical Analysis

Threat Vector: Unmonitored AI interactions and "Shadow AI" SaaS usage.

Affected Systems:

• Endpoints: Corporate devices accessing web-based AI services (e.g., ChatGPT, Claude, Bing Chat).
• Network: SSL/TLS traffic to known AI API endpoints.
• Data: Unstructured data (documents, code) being uploaded to external LLMs.

Severity: High. While a data upload to an AI tool isn't always a "breach," the loss of control over sensitive IP constitutes a significant compliance violation (GDPR, HIPAA) and a competitive risk.

Risk Details:

• Data Leakage: LLMs may retain input data to train future models, potentially exposing your secrets to other users or the public.
• Prompt Injection: Malicious actors crafting inputs to manipulate the AI into bypassing security controls or revealing system prompts.
• Supply Chain Risks: Using third-party AI libraries or models that contain hidden backdoors.

Defensive Monitoring

To protect against these emerging threats, SOC teams need visibility into AI usage. You cannot secure what you cannot see. Below are detection mechanisms, SIGMA rules, and hunting queries to identify Shadow AI and potential data leakage within your environment.

SIGMA Rules

These SIGMA rules are designed to run in SIEMs (Splunk, Elastic, Sentinel) to detect connections to high-risk AI endpoints and potential data leakage patterns.

---
title: Potential Shadow AI Usage via Network Connection
id: 8c5d9a23-1f72-4e3a-b8c5-2d2f3b4c5d6e
status: experimental
description: Detects endpoints making connections to known public Generative AI domains, indicating potential unsanctioned Shadow AI usage.
references:
  - https://www.gartner.com/en/newsroom/press-releases/2024-01-15-gartner-predicts-ai-will-drive-half-of-incident-response-efforts-by-2028
author: Security Arsenal
date: 2026-05-01
tags:
  - attack.exfiltration
  - attack.t1567.002
logsource:
  category: network_connection
  product: windows
detection:
  selection:
    Initiated: 'true'
    DestinationHostname|contains:
      - 'openai.com'
      - 'chatgpt.com'
      - 'anthropic.com'
      - 'bard.google.com'
  condition: selection
falsepositives:
  - Authorized use of AI tools by marketing or development teams (whitelist specific subnets if necessary)
level: medium
---
title: High Volume Data Upload to AI Endpoint
id: 3a2b1c4d-5e6f-4a7b-8c9d-0e1f2a3b4c5d
status: experimental
description: Detects suspicious outbound traffic patterns indicative of large data uploads to Generative AI APIs, potentially signaling data exfiltration.
references:
  - https://attack.mitre.org/techniques/T1567/
author: Security Arsenal
date: 2026-05-01
tags:
  - attack.exfiltration
  - attack.t1048.003
logsource:
  category: proxy
  product: suricata
detection:
  selection_dest:
    destination.domain|contains:
      - 'api.openai.com'
      - 'api.anthropic.com'
  selection_vol:
    sc_bytes|gt: 1000000  # Threshold: Upload size greater than 1MB
  condition: all of selection*
falsepositives:
  - Legitimate bulk processing by approved AI applications
level: high

KQL Queries (Microsoft Sentinel)

Use these KQL queries to investigate AI usage within your Microsoft 365 or Azure environment. This query checks for sign-ins or access patterns related to AI services, specifically monitoring for "Copilot" usage which can be a double-edged sword.

// Detect access to known Generative AI domains via Proxy or Firewall logs
let AIDomains = dynamic(["openai.com", "chatgpt.com", "anthropic.com", "claude.ai", "bing.com/chat"]);
DeviceNetworkEvents
| where RemoteUrl has_any (AIDomains)
| where ActionType == "ConnectionSuccess"
| summarize Count = count(), Timestamp = max(TimeGenerated) by DeviceName, InitiatingProcessAccountName, RemoteUrl
| order by Count desc
| project Timestamp, DeviceName, User = InitiatingProcessAccountName, AI_Service = RemoteUrl, Access_Count = Count

Velociraptor VQL Hunt Queries

Velociraptor is excellent for endpoint hunting. These VQL artifacts allow you to scan local browser history and running processes to identify if users are interacting with unauthorized AI tools.

-- Hunt for browser history entries containing AI-related keywords
SELECT
  Timestamp,
  Url,
  Title,
  BrowserType
FROM artifact(Browser.History.Chromium())
WHERE Url =~ 'openai'
   OR Url =~ 'chatgpt'
   OR Url =~ 'anthropic'
   OR Url =~ 'claude'
   OR Title =~ 'Chat'
ORDER BY Timestamp DESC


-- Hunt for active processes communicating with AI infrastructure
SELECT
  Pid,
  Name,
  CommandLine,
  Username,
  Exe
FROM pslist()
WHERE Exe =~ 'chrome'
   OR Exe =~ 'msedge'
   OR Exe =~ 'firefox'
-- Note: Further correlation with netstat would be required for destination IP checks,
-- but this isolates the browser processes capable of accessing web-based AI.

PowerShell Remediation/Verification Script

This script helps administrators check if specific endpoints (via Hosts file or DNS) or if Microsoft Copilot is enabled for specific users (requires Exchange Online module).

<#
.SYNOPSIS
    Check for indications of Shadow AI or unapproved AI access.
.DESCRIPTION
    This script checks the local hosts file for redirects to AI domains 
    and attempts to audit M365 Copilot status if credentials are available.
#>

# Check Hosts file for entries related to AI (Potential block or redirect)
$HostsPath = "$env:SystemRoot\System32\drivers\etc\hosts"
if (Test-Path $HostsPath) {
    $AI_Domains = @("openai", "chatgpt", "anthropic")
    $Content = Get-Content $HostsPath
    $Matches = $Content | Where-Object { $_ -match ($AI_Domains -join '|') }
    
    if ($Matches) {
        Write-Host "[WARNING] Found entries related to AI domains in Hosts file:" -ForegroundColor Yellow
        $Matches
    } else {
        Write-Host "[INFO] No AI-related entries found in Hosts file." -ForegroundColor Green
    }
}

# Note: Auditing M365 Copilot requires Connect-ExchangeOnline and specific RBAC roles.
# This section serves as a logical placeholder for enterprise auditors.
Write-Host "[INFO] To audit Microsoft Copilot access, run: Get-User | Select-Object DisplayName, CopilotStatus (requires Graph API)"

Remediation Steps

Gartner’s advice is clear: security teams must be involved from the start. Here is how to remediate and protect your organization:

1. Establish an AI Acceptable Use Policy (AUP): Clearly define what data can and cannot be entered into public AI models. Explicitly prohibit the input of PII, PHI, or sensitive code.
2. Implement Data Loss Prevention (DLP): Configure DLP policies (in Microsoft Purview, Cisco, or similar) to scan content moving to the cloud. Block content containing credit card numbers or confidential source code from being uploaded to known AI endpoints.
3. Shadow Discovery: Use the CASB (Cloud Access Security Broker) capabilities in your SSE or SASE platform to discover unsanctioned AI applications being used by your employees.
4. AI Governance Board: Create a cross-functional team including Security, Legal, and IT to evaluate every new AI tool before it is adopted.
5. Network Controls: If business requirements allow, block access to public, high-risk Generative AI sites at the proxy/firewall level and route all AI traffic through an enterprise-sanctioned gateway.

Executive Takeaways

• Proactive Governance: Waiting for an incident to react is insufficient. By 2028, half of IR will be AI-related; building the monitoring stack today is critical.
• Visibility is Key: You cannot stop Shadow AI if you don't know it's happening. Implement SIGMA rules and network monitoring immediately.
• Defense in Depth: Combine technical controls (DLP, Blocking) with administrative controls (Policy, Training) to mitigate the risk.

Related Resources

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