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Introduction:
The cybersecurity industry has developed a compulsive habit: renaming unresolved operational failures as breakthrough innovations. At RSA Conference, the term “agentic” has become the latest buzzword, following the paths of AI and SOAR before it. This cycle creates the illusion of progress while leaving core challenges—fragmented visibility, alert fatigue, and broken identity models—unaddressed.
Learning Objectives:
- Analyze the gap between marketing-driven cybersecurity terminology and actual operational improvements.
- Implement practical identity hardening and alert reduction techniques that solve persistent problems.
- Build an “agentic” framework focused on execution, validation, and business alignment rather than just automation.
You Should Know:
- Deconstructing the “Agentic” Hype: From Buzzword to Operational Reality
The conversation around “agentic” systems promises autonomous security operations, but without foundational controls, it simply adds another layer of complexity. The reality is that true autonomy requires immutable identity foundations, deterministic response playbooks, and closed-loop validation. Before deploying any autonomous tool, you must ensure that your environment can handle delegated authority without introducing new risks.
Start by auditing your current identity model. On Linux, examine privilege escalation paths using:
List all users with sudo privileges grep -r '^sudo' /etc/group Check for weak password policies grep '^PASS_MAX_DAYS' /etc/login.defs Review recent authentication logs for anomalies sudo journalctl -u sshd --since "24 hours ago"
On Windows, use PowerShell to assess identity hygiene:
Find users with administrative privileges Get-LocalGroupMember -Group "Administrators" Check for stale accounts that could be exploited Search-ADAccount -AccountInactive -TimeSpan 90.00:00:00 | Select Name, LastLogonDate
The step‑by‑step guide here is to remediate any misconfigurations: rotate privileged credentials, enforce MFA, and implement just-in-time (JIT) access. Without this, an “agentic” system inherits the same vulnerabilities that attackers exploit daily.
2. Automating SOAR Without the Marketing Overhead
SOAR (Security Orchestration, Automation, and Response) was last year’s salvation, yet most implementations still struggle with fragmented tooling and manual handoffs. Instead of purchasing a new SOAR platform with “agentic” branding, focus on automating the most repetitive tasks using native tools and scripts.
For example, automate the containment of a malicious IP address using a simple API call to your firewall. Using Python with requests:
import requests
firewall_ip = "192.168.1.1"
api_key = "your_api_key"
malicious_ip = "10.0.0.100"
headers = {"Authorization": f"Bearer {api_key}"}
data = {"address": malicious_ip, "action": "block"}
response = requests.post(f"https://{firewall_ip}/api/v1/blocklist", json=data, headers=headers)
print(response.json())
This script replaces a manual 10‑minute task with a sub‑second execution. The key is to integrate this into your SIEM so that alerts trigger automatic containment based on risk scores, reducing mean time to respond (MTTR). Create a playbook that validates the action—like checking if the IP appears in threat intelligence feeds—before execution. This transforms a marketing buzzword into a measurable reduction in operational friction.
- Building Context Across Tools to Eliminate Alert Fatigue
Alert fatigue stems from a lack of context; security teams receive thousands of isolated events without the narrative that connects them. To solve this, you need to normalize data from disparate sources into a single, enriched view. This is where a lightweight data pipeline can outperform an expensive “agentic” platform.
Use `syslog-ng` or `rsyslog` on Linux to aggregate logs from network devices, endpoints, and cloud services into a central location. Configure log filtering to drop noise:
Example rsyslog.conf rule to drop repetitive DHCP logs if ($programname == 'dhcpd') and ($msg contains 'DHCPDISCOVER') then stop
For cloud environments, leverage AWS CloudTrail or Azure Monitor to stream logs to a data lake. Then, use a tool like `jq` to parse and correlate events:
Extract failed login attempts from AWS CloudTrail logs cat cloudtrail.json | jq '.Records[] | select(.eventName=="ConsoleLogin" and .responseElements.ConsoleLogin=="Failure")'
Combine this with endpoint detection and response (EDR) data to map a single attacker’s movement across systems. The result is a contextualized alert that tells a story, allowing analysts to focus on genuine threats rather than sifting through thousands of false positives.
4. Strengthening Operational Discipline Through Immutable Infrastructure
Weak operational discipline is often the root cause behind security gaps. Teams frequently rely on manual configuration changes that drift over time, creating vulnerabilities. Immutable infrastructure—where servers are never updated but replaced—can enforce consistency and reduce the attack surface.
On Linux, use `terraform` to define infrastructure as code (IaC). Create a base AMI that includes all security hardening (e.g., CIS benchmarks) and deploy it with a version tag:
resource "aws_instance" "web_server" {
ami = "ami-0c55b159cbfafe1f0" Hardened AMI
instance_type = "t2.micro"
tags = {
Name = "WebServer"
Version = "2025-03-01"
}
}
For Windows, use `Desired State Configuration (DSC)` to enforce security settings:
Configuration SecureServer {
Node "web-server" {
WindowsFeature IIS {
Ensure = "Present"
Name = "Web-Server"
}
Registry DisableGuestAccount {
Ensure = "Present"
Key = "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon"
ValueName = "AutoAdminLogon"
ValueData = "0"
}
}
}
The operational discipline comes from the process: no manual changes are allowed; all updates require a pull request against the code repository. This eliminates configuration drift and ensures that every server is deployed with the same hardened state.
- Aligning Security with Business Operations Through Measurable Metrics
The final gap in the rebranding cycle is the lack of alignment with business outcomes. Security teams often measure success by the number of alerts handled or vulnerabilities patched, which doesn’t resonate with business leadership. Shift to metrics that reflect business impact: time to remediation, number of incidents with real impact, and reduction in operational friction.
Implement a dashboard that tracks:
- Mean time to remediate (MTTR) critical vulnerabilities.
- Number of business-impacting incidents per quarter.
- Automation rate: percentage of alerts handled without human intervention.
Use a tool like `Grafana` connected to your SIEM database to visualize these metrics. For example, a simple SQL query to calculate MTTR:
SELECT AVG(EXTRACT(EPOCH FROM (resolved_at - created_at))) AS avg_mttr_seconds FROM incidents WHERE severity = 'critical' AND created_at > NOW() - INTERVAL '30 days';
Presenting this data to stakeholders shifts the conversation from “what are we buying” to “how are we improving.” This is the real innovation—not the buzzword, but the measurable progress.
What Undercode Say:
- The cybersecurity industry’s rebranding cycle masks a failure to solve foundational problems like identity management and alert fatigue.
- True progress requires operational discipline, automation of repetitive tasks, and metrics that tie security efforts to business outcomes.
The industry’s pivot to “agentic” systems is a double‑edged sword. If adopted as a marketing label, it will perpetuate the same cycle of underdelivery. However, if used as a catalyst to finally address automation, validation, and control structures, it could force the operational maturity that has been lacking. The challenge lies in resisting the urge to buy a solution and instead focusing on the fundamentals: immutable infrastructure, context‑aware alerting, and measurable improvement. The organizations that succeed will be those that treat new terms as opportunities to revisit old problems with a fresh lens—not as excuses to avoid solving them.
Prediction:
Within the next 18 months, “agentic” security platforms will face intense scrutiny as early adopters realize they cannot operate autonomously without mature identity and process foundations. The market will shift toward integrated solutions that focus on closed‑loop validation rather than standalone automation, and vendors who fail to provide measurable reduction in MTTR and operational friction will be abandoned. The real winners will be organizations that use the buzzword as a forcing function to invest in data normalization, playbook automation, and business‑aligned metrics.
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