Bridging the Gap: Moving from Siloed Scans to Agentic Validation
Hey everyone,
I was just reading the latest piece on why security validation is shifting toward "agentic" workflows, and it really hit home regarding the stack sprawl we deal with. Right now, most of us have a Breach and Attack Simulation (BAS) tool isolated in one corner, a vuln scanner feeding data into an ASM platform elsewhere, and maybe an annual pentest report that gathers dust. None of these tools talk to each other, leaving us to manually correlate the data.
The move toward "agentic" validation is about autonomous agents coordinating these layers. Instead of just flagging a CVE like CVE-2026-1234, an agent should automatically query the attack surface management (ASM) platform to find exposed instances and then trigger the BAS tool to validate exploitability against that specific target.
I’m currently trying to script a basic version of this myself to bridge our BAS and scanner. Here is a rough Python concept where an agent logic queries a vuln API and triggers a validation task:
import requests
def check_and_validate(cve_id):
# Query Vulnerability Scanner for affected assets
vuln_data = requests.get(f"https://api.scanner.local/vulns/{cve_id}").()
if vuln_data['criticality'] > 8.0:
targets = [asset['ip'] for asset in vuln_data['assets']]
# Trigger BAS validation for specific targets
for target in targets:
payload = {"cve": cve_id, "target": target}
requests.post("https://api.bas.local/simulate", =payload)
print(f"Agent initiated validation for {target} on {cve_id}")
check_and_validate("CVE-2026-1234")
Is anyone else seeing vendors actually deliver on this "agentic" promise, or are we all just building custom glue code to make our stacks talk to each other?
We're seeing this exact pain in the SOC. We get flooded with alerts from the vuln scanner but have zero context on exploitability. We started pushing the output into a SIEM rule that checks for active BAS signatures matching the CVEs. It’s not fully autonomous yet, but it cuts down the noise significantly. The 'agent' model is the end game, but integration APIs are still too proprietary.
From a pentester's perspective, I'm skeptical. An agent can validate a known CVE, sure, but can it chain a misconfigured S3 bucket with an SSRF to achieve RCE? The 'agentic' hype feels like it's trying to replace the creative chaos of a human attacker. Until they can handle logic gaps and business logic flaws, these are just automated scanners with better marketing.
I manage security for an MSP, and tool fatigue is real. I’d pay a premium for a platform that takes the ASM data and automatically runs the BAS checks without me logging into three different portals. Currently, I use a PowerShell script to pull the critical CVE list and CSV it, but having it act autonomously would save hours a week.
The missing link is often context from the wild. We shouldn't just validate everything; we should validate based on active threats. We're feeding our TI feeds directly into the orchestration layer so that when a specific CVE is observed being exploited in the wild by relevant actors, the "agent" automatically prioritizes that validation.
For example, we tag assets exposed to these active CVEs using a query like this before triggering the test:
Vulnerability | where CVE in (ActiveThreatIntel) | summarize count() by Asset
@whatahey raises a valid point regarding the complexity of chaining vulnerabilities. Agentic validation isn't just about individual CVEs; it's about emulating attacker behavior. The real power lies in the orchestration layer tying these tools together. We're using API-driven workflows to trigger specific BAS simulations based on high-value assets identified in our ASM. This prioritizes validation efforts on what matters most, rather than just scanning for scanning's sake. This KQL query, for example, helps us identify those critical assets first:
DeviceInfo
| where RiskScore > 80
| project DeviceName, IP, RiskScore, OS
| order by RiskScore desc
It's about context-aware validation, not just automation.
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