How to Secure AI Implementations and Protect Against Automated Threats: Insights from RSAC 2026

As AI dominated the conversations at RSAC 2026, security professionals found themselves at a critical juncture. While artificial intelligence offers unprecedented opportunities for enhancing threat detection and response, it simultaneously opens new attack surfaces that defenders must address. This post examines the defensive implications of AI's growing role in cybersecurity and provides actionable guidance for protecting your organization.

The Growing AI Security Challenge

The theme at this year's RSAC was clear: AI has fundamentally changed the threat landscape. What's particularly concerning for defenders is that while AI capabilities are advancing rapidly, many organizations are implementing these technologies without adequate security controls in place. The absence of the US government at this year's conference highlights the need for private sector leadership in establishing AI security standards.

Technical Analysis

The rapid integration of AI and machine learning into security operations creates a complex defensive environment. Organizations must protect against several emerging threat vectors:

Data Poisoning: Attackers manipulating training datasets to produce flawed models that make incorrect security decisions
Adversarial AI Attacks: Crafted inputs designed to evade AI-powered detection systems
Automated Reconnaissance: AI tools that can discover vulnerabilities at unprecedented speed and scale
AI-Generated Attacks: Phishing campaigns, malware, and exploits created or optimized by AI systems
Model Extraction: Attacks where adversaries attempt to steal proprietary AI models

These threats are particularly challenging because they exploit the very technologies many organizations rely on for defense, creating a complex security environment where traditional controls may be insufficient.

Defensive Monitoring

To protect your environment from AI-related threats, implement the following detection mechanisms:

SIGMA Rules

YAML

---
title: Suspicious AI Tool Execution
id: 7f9a8b3c-2d1e-4f5a-9b6c-1d2e3f4a5b6c
status: experimental
description: Detects execution of AI/ML tools that may be used for reconnaissance or attacks
references:
  - https://attack.mitre.org/techniques/T1059/
author: Security Arsenal
date: 2026/03/29
tags:
  - attack.execution
  - attack.t1059.001
logsource:
  category: process_creation
  product: windows
detection:
  selection:
    Image|contains:
      - '\python.exe'
      - '\python3.exe'
    CommandLine|contains:
      - 'tensorflow'
      - 'pytorch'
      - 'scikit-learn'
      - 'keras'
      - 'pandas'
      - 'numpy'
  filter:
    User|contains:
      - 'NT AUTHORITY\SYSTEM'
      - 'AUTHORI'
falsepositives:
  - Legitimate data science work
  - Authorized security research
level: medium
---
title: Suspicious AI Data Access Patterns
id: 8a0b9c4d-3e2f-5a6b-0c7d-2e3f4a5b6c7d
status: experimental
description: Detects unusual data access patterns that may indicate data poisoning attempts
references:
  - https://attack.mitre.org/techniques/T1005/
author: Security Arsenal
date: 2026/03/29
tags:
  - attack.collection
  - attack.t1005
logsource:
  category: file_event
  product: windows
detection:
  selection:
    TargetFilename|contains:
      - '\models\'
      - '\datasets\'
      - '\training\'
      - '\ml\'
  filter:
    User|contains:
      - 'NT AUTHORITY\SYSTEM'
falsepositives:
  - Authorized ML model training
  - Legitimate data scientist activities
level: medium
---
title: Potential AI-Based Reconnaissance Tool
id: 9b1c0d5e-4f3a-6b7c-1d8e-3f4a5b6c7d8e
status: experimental
description: Detects potential AI-based reconnaissance tools that automate discovery
references:
  - https://attack.mitre.org/techniques/T1595/
author: Security Arsenal
date: 2026/03/29
tags:
  - attack.reconnaissance
  - attack.t1595.001
logsource:
  category: network_connection
  product: windows
detection:
  selection:
    DestinationPort|between:
      - 1
      - 65535
    Initiated: 'true'
    Image|contains:
      - '\python.exe'
      - '\python3.exe'
    CommandLine|contains:
      - 'nmap'
      - 'scapy'
      - 'requests'
      - 'scrape'
falsepositives:
  - Authorized network scanning
  - Legitimate development testing
level: high

KQL Queries

KQL — Microsoft Sentinel / Defender

// Detect unusual AI tool execution patterns
DeviceProcessEvents
| where (ProcessName has "python.exe" or ProcessName has "python3.exe")
| where ProcessCommandLine has_any ("tensorflow", "pytorch", "scikit-learn", "keras")
| summarize count(), min(Timestamp), max(Timestamp) by DeviceId, InitiatingProcessAccountName, ProcessCommandLine
| where count_ > 10

// Detect potential data access for ML models
DeviceFileEvents
| where FolderPath has_any ("\\models\\", "\\datasets\\", "\\training\\", "\\ml\\")
| where ActionType in ("FileCreated", "FileModified", "FileAccessed")
| project Timestamp, DeviceId, InitiatingProcessAccountName, FolderPath, FileName, ActionType
| sort by Timestamp desc

// Detect network connections from AI tools
DeviceNetworkEvents
| where (InitiatingProcessFileName has "python.exe" or InitiatingProcessFileName has "python3.exe")
| where InitiatingProcessCommandLine has_any ("nmap", "scapy", "requests", "scrape")
| summarize count() by DeviceId, InitiatingProcessAccountName, RemoteUrl, RemoteIP, RemotePort
| where count_ > 5

Velociraptor VQL

VQL — Velociraptor

-- Hunt for AI/ML tool executions
SELECT Name, Pid, CommandLine, Username, Exe, CreateTime
FROM pslist()
WHERE Name =~ "python.exe" 
   OR Name =~ "python3.exe"
   AND CommandLine =~ "(tensorflow|pytorch|scikit-learn|keras|pandas|numpy)"

-- Hunt for data access patterns in ML directories
SELECT FileName, FullPath, Size, Mode.Mtime, Mode.Atime, Username
FROM glob(globs='C:/Users/**/models/**', 'C:/Users/**/datasets/**', 'C:/Users/**/training/**')
WHERE NOT FullName =~ "\\AppData\\Local\\Microsoft\\WindowsApps\\"

-- Hunt for suspicious network connections from AI tools
SELECT Pid, Name, RemoteAddress, RemotePort, Username, StartTime
FROM netstat()
WHERE (Name =~ "python.exe" OR Name =~ "python3.exe")
  AND CommandLine =~ "(nmap|scapy|requests|scrape)"

PowerShell Scripts

PowerShell

# Script to inventory AI/ML tools in the environment
function Get-AIToolsInventory {
    param (
        [string[]]$ComputerName = $env:COMPUTERNAME
    )
    
    $AIKeywords = @("tensorflow", "pytorch", "scikit-learn", "keras", "pandas", "numpy")
    $Results = @()
    
    foreach ($Computer in $ComputerName) {
        $Processes = Get-CimInstance -ClassName Win32_Process -ComputerName $Computer
        
        foreach ($Process in $Processes) {
            if ($Process.Name -match "python.exe" -or $Process.Name -match "python3.exe") {
                if ($AIKeywords | Where-Object { $Process.CommandLine -like "*$_*" }) {
                    $Results += [PSCustomObject]@{
                        ComputerName = $Computer
                        ProcessName = $Process.Name
                        ProcessId = $Process.ProcessId
                        CommandLine = $Process.CommandLine
                        ExecutablePath = $Process.ExecutablePath
                        Owner = Invoke-CimMethod -InputObject $Process -MethodName GetOwner | Select-Object -ExpandProperty User
                    }
                }
            }
        }
    }
    
    return $Results
}

# Example usage
Get-AIToolsInventory | Format-Table -AutoSize

# Script to check for unauthorized data access to ML directories
function Get-MLDirectoryAccess {
    $MLDirectories = @(
        "$env:USERPROFILE\models",
        "$env:USERPROFILE\datasets",
        "$env:USERPROFILE\training",
        "C:\models",
        "C:\datasets"
    )
    
    $Results = @()
    
    foreach ($Dir in $MLDirectories) {
        if (Test-Path $Dir) {
            $Events = Get-WinEvent -FilterHashtable @{
                LogName = 'Security'
                ID = 4663
                Path = $Dir
            } -ErrorAction SilentlyContinue
            
            foreach ($Event in $Events) {
                $Results += [PSCustomObject]@{
                    TimeCreated = $Event.TimeCreated
                    Directory = $Dir
                    SubjectUser = $Event.Properties[1].Value
                    ObjectName = $Event.Properties[4].Value
                    AccessMask = $Event.Properties[5].Value
                }
            }
        }
    }
    
    return $Results
}

# Example usage
Get-MLDirectoryAccess | Format-Table -AutoSize

Remediation Steps

Implement Data Governance: Establish strict controls for AI training data, including validation processes and access restrictions.
Monitor Model Behavior: Implement anomaly detection specifically designed to identify when AI models make unexpected decisions.
Secure AI Infrastructure: Apply the same security controls to your AI/ML environments as you would to production systems.
Human Oversight: Maintain human review processes for critical security decisions, ensuring AI recommendations are validated.
Regular Assessments: Conduct security assessments focused on AI implementations, including adversarial testing.
Model Versioning: Maintain strict version control and integrity verification for all AI models in production.
Input Validation: Implement robust validation for all inputs to AI systems to prevent adversarial manipulation.
Community Engagement: As emphasized at RSAC, participate in the security community to stay informed about AI-related threats and defenses.

Executive Takeaways

The RSAC 2026 conference highlighted that while AI is transforming cybersecurity, the human element remains critical. Organizations must balance automation with expert oversight, leveraging AI to enhance—not replace—skilled security professionals. As one presenter noted, "AI can find the patterns, but humans must understand the context."

Defenders should focus on implementing layered defenses that account for AI-specific threats while leveraging these technologies to improve detection and response capabilities. The most successful organizations will be those that thoughtfully integrate AI into their security operations while maintaining robust human oversight.

Related Resources

Security Arsenal Alert Triage Automation AlertMonitor Platform Book a SOC Assessment platform Intel Hub

How to Secure AI Implementations and Protect Against Automated Threats: Insights from RSAC 2026

How to Secure AI Implementations and Protect Against Automated Threats: Insights from RSAC 2026

The Growing AI Security Challenge

Technical Analysis

Defensive Monitoring

SIGMA Rules

KQL Queries

Velociraptor VQL

PowerShell Scripts

Remediation Steps

Executive Takeaways

Related Resources

Is your security operations ready?