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Introduction:
The modern enterprise has evolved into an always-on, hyper-connected ecosystem where AI agents and non-human identities increasingly outnumber human users, and activity never stops. At Black Hat USA 2026, Vectra AI demonstrated how its AI-1ative security platform addresses the fundamental shift in cyber risk—where attackers no longer break in but log in, exploit trust, and blend in with legitimate behavior. This article explores the technical depth behind AI-driven threat detection, unified observability, and the practical steps security teams can take to uncover attack gaps hiding in their environments.
Learning Objectives:
- Understand how AI-1ative security platforms correlate network, identity, and cloud telemetry to detect attacker behaviors across the full cyber kill chain
- Learn to implement unified observability strategies that combine flow-based scale with packet-level context for preemptive threat exposure management
- Master practical techniques for discovering and tracking AI agents, automating investigation workflows, and hardening cloud and identity infrastructure against AI-powered attacks
You Should Know:
1. Unified Observability: The Foundation of Preemptive Security
Vectra AI’s approach to unified observability anchors on telemetry from the network (packets and flows) as ground truth, augmented with identity and cloud telemetry from platforms such as Entra ID, Microsoft 365, AWS, and Azure. This allows security teams to reconstruct behaviors across domains, correlate identities, hosts, and services into a unified narrative, and maintain visibility into east-west and ephemeral traffic.
Step‑by‑step guide to implementing unified observability:
Step 1: Deploy network detection sensors – Position Vectra AI sensors (physical or virtual) at key network aggregation points to capture flow and packet-level data across data centers, campus networks, and cloud VPCs.
Step 2: Integrate identity telemetry – Connect Entra ID (Azure AD) and Microsoft 365 using the Vectra AI Platform’s native integrations. This provides visibility into authentication patterns, privilege escalations, and OAuth token abuse.
Step 3: Enable cloud-1ative detection – For AWS, Azure, and GCP environments, deploy Vectra AI’s cloud detection and response (CDR) agents that run natively within the cloud infrastructure to monitor API calls, storage access, and compute activity.
Step 4: Configure AI-driven stitching – The platform automatically links behaviors across network activity, identity telemetry, cloud operations, and SaaS interactions into a single unified narrative using AI-driven stitching.
Step 5: Set up continuous threat hunting – Use pre-built, behavior-based hunts that deliver results in seconds, turning threat hunting into a continuous, proactive practice.
Linux/Windows Commands for Network Visibility:
Linux: Capture network flows for analysis
sudo tcpdump -i eth0 -1n -s0 -w capture.pcap
Linux: View active network connections and processes
sudo netstat -tunap | grep ESTABLISHED
Linux: Monitor real-time network traffic with filtering
sudo tshark -i eth0 -Y "tcp.port == 443 or tcp.port == 80"
Windows: View current network connections with process IDs
netstat -ano | findstr ESTABLISHED
Windows: Capture network traffic using netsh (requires Message Analyzer or similar)
netsh trace start capture=yes tracefile=C:\capture.etl
Windows: List all listening ports and associated processes
Get-1etTCPConnection | Where-Object {$_.State -eq "Listen"} | Select-Object LocalPort, OwningProcess
2. Attack Signal Intelligence: Moving Beyond Anomalies
Traditional security approaches that rely on signatures and anomaly detection generate overwhelming noise and require constant human maintenance. Vectra AI’s Attack Signal Intelligence takes a fundamentally different approach: start with attacker methods (not the data), identify the behaviors that define them, and then apply the right AI models for high-fidelity detection.
Step‑by‑step guide to leveraging Attack Signal Intelligence:
Step 1: Understand the AI model stack – Vectra AI applies different models for different attacker behaviors: recurrent neural networks (LSTMs) for command-and-control traffic that behaves like time-series data; multi-dimensional baselines for identity privilege abuse; and relationship graphs for lateral movement across multi-cloud environments.
Step 2: Configure detection thresholds – The platform continuously and automatically monitors for attacker methods using Security AI models programmed with an understanding of attacker TTPs (tactics, techniques, and procedures).
Step 3: Enable AI-driven prioritization – Another layer of AI combines an understanding of the organization’s environment with threat models and human threat intelligence to automatically surface and prioritize threats based on severity and impact.
Step 4: Implement automated triage – AI-Triage initially examines all detections to look for patterns that may indicate benign behavior, significantly reducing false positives.
Step 5: Monitor identity-based attacks – The platform now detects LDAP queries targeting Active Directory accounts that lack Kerberos pre-authentication—a common step in AS-REP roasting attacks—and identifies reconnaissance of AD users with servicePrincipalNames (SPNs), a precursor to Kerberoasting.
Linux/Windows Commands for Identity Security:
Linux: Check for suspicious LDAP queries
sudo tcpdump -i eth0 port 389 or port 636 -1n -v
Linux: Audit Kerberos ticket requests
sudo journalctl -u krb5-kdc | grep -i "pre-authentication"
Windows (PowerShell): Check for Kerberos pre-authentication disabled accounts
Get-ADUser -Filter {DoesNotRequirePreAuth -eq $true} -Properties DoesNotRequirePreAuth
Windows (PowerShell): Find accounts with SPNs (potential Kerberoasting targets)
Get-ADUser -Filter {ServicePrincipalName -1e "$null"} -Properties ServicePrincipalName
Windows (PowerShell): Audit failed login attempts
Get-EventLog -LogName Security -InstanceId 4625 | Select-Object TimeGenerated, @{Name="Account";Expression={$_.ReplacementStrings[bash]}}
Linux: Monitor authentication failures
sudo grep "Failed password" /var/log/auth.log | tail -20
3. EDR Process Correlation: Bridging Network and Endpoint
One of the most significant advancements demonstrated at Black Hat is automatic EDR Process Correlation. When Vectra AI identifies suspicious network behavior, it automatically queries CrowdStrike telemetry for that specific host, analyzes the process activity, and identifies the most probable process that triggered the detection.
Step‑by‑step guide to automating investigation workflows:
Step 1: Integrate EDR with Vectra AI – Connect CrowdStrike or Microsoft Defender for Endpoint to the Vectra AI Platform. This enables the automatic correlation of network detections with endpoint process context.
Step 2: Analyze detection context – When a detection appears, analysts immediately see: probable process name, process creation timestamp, full command line, file path, account name (privilege level), parent process, and SHA256 hash.
Step 3: One-click pivot to EDR – From the Vectra AI detection console, analysts can pivot directly to the full process tree and forensic timeline in CrowdStrike or Microsoft Defender.
Step 4: Investigate disguised threats – The command line context alone often reveals malicious intent. For example, a process disguised as “MicrosoftEdgeUpdate.exe” with a command line pointing to a C2 domain and a 5-minute beacon interval immediately confirms a persistent threat.
Step 5: Automate response actions – Use the correlated intelligence to trigger automated containment actions, such as isolating the endpoint or blocking the identified malicious process.
Windows Commands for Endpoint Investigation:
PowerShell: Get detailed process information
Get-Process | Select-Object Name, ID, Path, StartTime | Sort-Object StartTime -Descending
PowerShell: Find processes with network connections
Get-1etTCPConnection | Where-Object {$<em>.State -eq "Established"} | ForEach-Object {
$proc = Get-Process -Id $</em>.OwningProcess -ErrorAction SilentlyContinue
[bash]@{Process=$proc.ProcessName; PID=$<em>.OwningProcess; LocalPort=$</em>.LocalPort; RemoteAddress=$_.RemoteAddress}
}
PowerShell: Check for suspicious scheduled tasks
Get-ScheduledTask | Where-Object {$<em>.State -eq "Running"} | Select-Object TaskName, TaskPath, @{Name="Actions";Expression={$</em>.Actions.Execute}}
PowerShell: Examine services for anomalies
Get-Service | Where-Object {$<em>.StartType -eq "Automatic" -and $</em>.Status -eq "Running"} | Select-Object Name, DisplayName, @{Name="BinaryPath";Expression={(Get-WmiObject Win32_Service -Filter "Name='$($_.Name)'").PathName}}
- Cloud Hardening and API Security for AI Infrastructure
As AI agents become first-class enterprise identities and increasingly powerful tools for attackers, securing the underlying cloud and API infrastructure is paramount. Vectra AI’s platform provides coverage across AWS, Google Cloud Platform, IBM Cloud, Microsoft Azure, Microsoft 365, and Oracle Cloud environments.
Step‑by‑step guide to hardening cloud environments:
Step 1: Implement least-privilege identity access – Use Vectra AI’s identity threat detection to identify risky misconfigurations, identity compromise, lateral movement, and data-exfiltration across cloud, SaaS, and on-premises environments.
Step 2: Configure firewall requirements – Vectra appliances should be blocked from direct Internet access, with access granted only from trusted workstations and authenticated sources. Allow outbound HTTPS (TCP/443) to `api.vectranetworks.com` and `update2.vectranetworks.com` for health monitoring and automatic security updates.
Step 3: Monitor AI agent activity – Discover and track AI agents across the modern network, detecting when attackers use and abuse AI agents to accelerate reconnaissance, lateral movement, and data access.
Step 4: Enable role-based access control – Implement granular RBAC with separate roles for API access, per-corpus access, per-agent access, and system-level platform roles.
Step 5: Continuous compliance monitoring – Use Vectra AI’s compliance briefs for AWS and other cloud platforms to maintain alignment with security frameworks and regulatory requirements.
Cloud Hardening Commands (AWS/Azure CLI):
AWS CLI: List all IAM users and their attached policies aws iam list-users --query 'Users[].[UserName, Arn]' --output table aws iam list-attached-user-policies --user-1ame <username> AWS CLI: Check for overly permissive security groups aws ec2 describe-security-groups --query 'SecurityGroups[?length(IpPermissions[?IpRanges[?CidrIp==<code>0.0.0.0/0</code>]])><code>0</code>]' AWS CLI: Identify unused IAM keys aws iam list-access-keys --user-1ame <username> | jq '.AccessKeyMetadata[].AccessKeyId' Azure CLI: List all role assignments az role assignment list --all --output table Azure CLI: Check for privileged roles az role assignment list --include-inherited --output table | grep -E "Owner|Contributor|Global Administrator" Azure CLI: Audit conditional access policies az rest --method get --url "https://graph.microsoft.com/v1.0/identity/conditionalAccess/policies" --headers "Content-Type=application/json"
5. Accelerated Response at Machine Speed
The final pillar of AI-1ative security is accelerated response. Vectra AI removes latency where it matters most: how quickly defenders get answers. By correlating and prioritizing activity based on attack progression, the platform enables earlier disruption of lateral movement and faster containment.
Step‑by‑step guide to accelerating incident response:
Step 1: Enable real-time correlation – The platform continuously processes telemetry as it is generated and connects activity across domains without waiting for batch jobs or manual review.
Step 2: Use the Vectra AI Assistant – Run natural-language investigations and obtain immediate, contextual answers, significantly reducing responder latency.
Step 3: Implement automated containment – Configure playbooks that automatically trigger containment actions based on detection severity and confidence scores.
Step 4: Monitor attack progression – Prioritize risk based on how fast and how far an attack is progressing across the hybrid environment.
Step 5: Continuous improvement – Leverage Vectra MDR services for 24/7 AI-powered threat detection and hands-on expert support to eliminate false positives and reduce noise.
Linux/Windows Commands for Incident Response:
Linux: Quick forensic collection
sudo tar -czf /tmp/forensic_$(date +%Y%m%d_%H%M%S).tar.gz /var/log /etc/passwd /etc/shadow /var/spool/cron
Linux: Check for unauthorized sudo access
sudo grep "COMMAND" /var/log/auth.log | grep -v "root"
Linux: Identify recently modified files (potential malware)
sudo find / -type f -mtime -1 -ls 2>/dev/null | head -50
Windows (PowerShell): Collect forensic artifacts
Get-EventLog -LogName Security -1ewest 1000 | Export-Csv -Path C:\forensic\security_events.csv -1oTypeInformation
Windows (PowerShell): Check for persistence mechanisms
Get-ItemProperty -Path "HKLM:\Software\Microsoft\Windows\CurrentVersion\Run" | Select-Object PSChildName,
Get-ItemProperty -Path "HKCU:\Software\Microsoft\Windows\CurrentVersion\Run" | Select-Object PSChildName,
Windows (PowerShell): Identify suspicious scheduled tasks created recently
Get-ScheduledTask | Where-Object {$_.Date -gt (Get-Date).AddDays(-7)}
What Undercode Say:
- Attackers don’t break in; they log in. The modern threat landscape is defined by identity-based attacks where adversaries exploit valid credentials, OAuth tokens, and trust relationships to bypass traditional perimeter controls. Security strategies must shift from perimeter defense to identity-centric detection and response.
-
AI in security must be outcome-driven, not feature-driven. Adding more AI features doesn’t fix the core problems of alert fatigue and slow investigations. The most effective AI applications start with attacker methods and apply the right models to specific behaviors, reducing noise and surfacing real threats.
-
Unified observability is non-1egotiable. Attacks don’t live in one domain—they move across network, identity, SaaS, cloud, and AI infrastructure. Security teams need a unified view that correlates signals across these disparate surfaces into a single attack narrative.
-
EDR correlation closes the investigative gap. Manual correlation between network detections and endpoint processes is slow, error-prone, and unsustainable. Automatic process correlation converts “potentially suspicious” into “confirmed persistent threat” in seconds.
-
AI agents are the new attack surface. With AI agents becoming first-class enterprise identities, attackers are already learning to abuse them for reconnaissance, lateral movement, and data access. Discovering and tracking AI agents must become a standard security practice.
Prediction:
-
+1 AI-1ative security will become the dominant SOC architecture by 2028. Legacy SIEMs and signature-based detection tools cannot keep pace with AI-powered attackers operating at machine speed. Organizations that fail to adopt AI-driven behavioral detection will face increasing breach risks.
-
+1 Identity-based threat detection will see a 300% increase in investment. As attackers increasingly exploit identities rather than vulnerabilities, security budgets will shift dramatically toward identity threat detection and response (ITDR) capabilities.
-
-1 AI agent exploitation will emerge as a major attack vector within 12-18 months. The rapid integration of AI agents into enterprise workflows, combined with insufficient security controls, creates a massive and largely unsecured attack surface. Expect high-profile breaches involving compromised AI agents.
-
+1 Automated investigation workflows will reduce mean time to respond (MTTR) by 60-80%. The integration of network, identity, and endpoint telemetry with AI-driven correlation will dramatically accelerate incident response, turning hours of manual investigation into seconds of automated analysis.
-
-1 The skills gap in AI security will widen significantly. The complexity of AI-1ative security platforms and the specialized knowledge required to operate them will outpace the availability of trained professionals, creating a critical shortage of AI security talent.
▶️ Related Video (80% Match):
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