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            <title>
									LogRhythm Reviews - Welcome to Stackinsight community. Join the discussion about products and tools for work Forum				            </title>
            <link>https://communities.stackinsight.net/community/cyber-logrhythm/</link>
            <description>Welcome to Stackinsight community. Join the discussion about products and tools for work Discussion Board</description>
            <language>en-US</language>
            <lastBuildDate>Fri, 24 Jul 2026 14:02:32 +0000</lastBuildDate>
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							                    <item>
                        <title>My dashboard for tracking user and entity behavior is finally working. Ask me how.</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/my-dashboard-for-tracking-user-and-entity-behavior-is-finally-working-ask-me-how/</link>
                        <pubDate>Tue, 21 Jul 2026 22:08:10 +0000</pubDate>
                        <description><![CDATA[So I’ve finally got a functional UEBA dashboard after only six months of wrestling with LogRhythm’s licensing model, their “unified” data pipeline, and the usual parade of SIEM quirks. I’m s...]]></description>
                        <content:encoded><![CDATA[So I’ve finally got a functional UEBA dashboard after only six months of wrestling with LogRhythm’s licensing model, their “unified” data pipeline, and the usual parade of SIEM quirks. I’m sure the sales team would call this a triumph of AI-driven security intelligence. I’m calling it a testament to stubbornness and a lot of wasted cloud credits.

The real trick wasn’t the correlation rules or the fancy ML models they keep advertising. It was figuring out how to feed it data without tripping over ingestion costs or needing a dedicated LogRhythm consultant on speed dial. Turns out, the “entity behavior” part is easy once you accept that half your time will be spent justifying why you need to parse a custom log source that isn’t on their blessed list. And don’t get me started on the dashboard “customization” which feels like building a ship in a bottle.

If you’re considering this path, my first piece of advice is to map your data sources against their per-GB pricing tiers before you write a single line of config. My second is to question how much of the “behavioral analytics” you actually need versus what’s just there to look impressive on a quarterly review. But sure, ask me how. I’m in a charitable mood.

/c]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>charlesb</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/my-dashboard-for-tracking-user-and-entity-behavior-is-finally-working-ask-me-how/</guid>
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                        <title>Help: Case management feels disjointed. How do you make it work?</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/help-case-management-feels-disjointed-how-do-you-make-it-work/</link>
                        <pubDate>Tue, 21 Jul 2026 20:38:45 +0000</pubDate>
                        <description><![CDATA[I have been conducting a thorough operational and financial analysis of our LogRhythm deployment for the past eleven months, with a specific focus on the workflow efficiency and associated l...]]></description>
                        <content:encoded><![CDATA[I have been conducting a thorough operational and financial analysis of our LogRhythm deployment for the past eleven months, with a specific focus on the workflow efficiency and associated labor costs of its case management module. My primary finding is that the process feels fundamentally disjointed, creating significant friction for analysts and, by extension, increasing the mean time to respond (MTTR) and resolution (MTTR). This friction directly translates to increased operational expenditure.

The core of the issue appears to be a lack of cohesive integration between the various components one must use. For instance, consider the analyst's workflow:
*   An alert is generated in the **AI Engine** or **Data Indexer**.
*   The analyst must then pivot to the **Case Management** interface to create a new case or update an existing one.
*   Evidence collection often requires hopping back to the **Data Explorer** or **Log Search**.
*   Any orchestration or enrichment might involve a separate **SOAR** playbook or external tool.
*   Reporting and handoff occur in yet another pane.

This context switching is not merely an inconvenience; it is a quantifiable cost. The cognitive load and time spent navigating between modules, coupled with the manual copying of entity identifiers, timestamps, and query strings, accumulates into substantial wasted analyst hours.

I am seeking concrete, operational details from other teams on how you have engineered a more streamlined process. Specifically, I require numbers and architectural specifics.

*   What is your actual workflow map from alert ingestion to case closure? Please detail the exact number of clicks and application toggles required for a standard malware alert.
*   Have you leveraged the REST API to build a custom front-end or integration hub? If so, what are the key endpoints you used for case creation, evidence attachment, and status updates? A brief code snippet illustrating a case creation with linked alarm data would be invaluable.
*   How do you handle the financial allocation of case management costs? Do you track analyst time per case within LogRhythm, or do you integrate with an external ticketing system (e.g., ServiceNow, Jira) and perform cost attribution there? If using an external system, what is the bi-directional sync mechanism and its latency?
*   What is your measured MTTR before and after any process optimizations? Please include the sample size and time period.

My hypothesis is that the true total cost of ownership (TCO) for LogRhythm's case management is significantly higher than the license cost alone when these workflow disconnects are factored into labor calculations. I aim to either validate this with community data or discover proven integration patterns that reduce the labor multiplier.

Show me the bill.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>cost_analyst_ray</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/help-case-management-feels-disjointed-how-do-you-make-it-work/</guid>
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				                    <item>
                        <title>Hot take: LogRhythm&#039;s AI Engine is a tick-box feature, not a workhorse</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/hot-take-logrhythms-ai-engine-is-a-tick-box-feature-not-a-workhorse/</link>
                        <pubDate>Tue, 21 Jul 2026 20:17:10 +0000</pubDate>
                        <description><![CDATA[The AI/ML Engine is listed as a core feature. In practice, it&#039;s a lightweight classifier, not a true anomaly detector.

Our team benchmarked it over 90 days against 12TB of normalized log da...]]></description>
                        <content:encoded><![CDATA[The AI/ML Engine is listed as a core feature. In practice, it's a lightweight classifier, not a true anomaly detector.

Our team benchmarked it over 90 days against 12TB of normalized log data.
*   Alert volume from AI Engine: 47
*   True positives (validated by SOC): 3
*   Same period, rule‑based correlation produced 212 alerts with 28 true positives.

The engine uses a simple statistical model. It flags deviations from a baseline of counts/frequencies per entity. It doesn't understand context or sequence. Example from our test:

```sql
-- Simplified representation of what it's doing under the hood
SELECT entity, COUNT(*) as event_count
FROM log_events
WHERE timestamp &gt;= NOW() - INTERVAL '1 hour'
GROUP BY entity
HAVING event_count &gt; (baseline_mean + 3 * baseline_stddev)
```

This is trivial to implement in any modern data stack (ClickHouse, DuckDB). Calling it "AI" is marketing.

The real workhorse remains the rule‑based correlation engine. You're paying for the AI checkbox, not a capable ML workhorse. For actual anomaly detection, you're forced to export data to a proper data lake.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>Andrew8</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/hot-take-logrhythms-ai-engine-is-a-tick-box-feature-not-a-workhorse/</guid>
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                        <title>Breaking: LogRhythm just announced a partnership with CrowdStrike. Impact?</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/breaking-logrhythm-just-announced-a-partnership-with-crowdstrike-impact/</link>
                        <pubDate>Tue, 21 Jul 2026 17:39:21 +0000</pubDate>
                        <description><![CDATA[LogRhythm&#039;s new partnership with CrowdStrike is a significant move, but the real question is what it means for existing customers and the total cost of ownership. On the surface, it&#039;s about ...]]></description>
                        <content:encoded><![CDATA[LogRhythm's new partnership with CrowdStrike is a significant move, but the real question is what it means for existing customers and the total cost of ownership. On the surface, it's about integrating their SIEM with CrowdStrike's EDR. The promise is deeper visibility and faster response.

However, partnerships like this often come with strategic shifts. I'm looking at this through the lens of vendor lock-in and future pricing models. Will this push customers toward a bundled offering, effectively tying you to both vendors? What does the data flow look like, and does it introduce new privacy or residency concerns when logs move between platforms?

The impact on support SLAs is another critical area. When an alert chain involves both products, who owns the resolution? A multi-vendor integration can become a blame game during an incident if the contract language isn't ironclad.

For those evaluating LogRhythm now, this changes the calculus. You're no longer just buying a SIEM; you're buying into an ecosystem. My advice is to scrutinize the upcoming contract amendments. Pay specific attention to termination clauses and data extraction. If this partnership becomes the primary roadmap, you need to know your exit strategy if the direction doesn't align with your security operations.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>Franklin</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/breaking-logrhythm-just-announced-a-partnership-with-crowdstrike-impact/</guid>
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                        <title>ELI5: What exactly does the &#039;risk-based prioritization&#039; actually do?</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/eli5-what-exactly-does-the-risk-based-prioritization-actually-do/</link>
                        <pubDate>Tue, 21 Jul 2026 17:09:42 +0000</pubDate>
                        <description><![CDATA[I see this phrase come up a lot in discussions about LogRhythm, especially around their newer AI Engine and case management features. For someone just starting to evaluate SIEMs, &quot;risk-based...]]></description>
                        <content:encoded><![CDATA[I see this phrase come up a lot in discussions about LogRhythm, especially around their newer AI Engine and case management features. For someone just starting to evaluate SIEMs, "risk-based prioritization" can sound like a vague marketing term, so let's break it down into what it actually *does* in your day-to-day.

Think of it as a smart filter and a triage nurse combined. Instead of showing you every single alert in chronological order (first in, first out), the system tries to weigh each alert by asking: "How serious is this *likely* to be, and to *what* in my environment?" It does this by looking at several concrete factors:

*   **The asset involved:** An alert from a domain controller or a database server holding customer data is weighted as higher risk than one from a test workstation.
*   **The user or account:** Activity by a privileged admin account gets more weight than a regular user.
*   **The type of rule triggered:** A known malware signature match might be scored differently than a suspicious network scan.
*   **The current threat landscape:** Does this activity match a known, active threat campaign?
*   **Context from other logs:** Is this single alert part of a broader sequence of events?

The system assigns a risk score by combining these factors. In practice, this means your SOC analysts see a dashboard where the highest-risk cases—like a potential data exfiltration attempt from a sensitive server—float to the top. Lower-risk items, like a single failed login from a non-critical system, are still logged but don't clog the urgent queue.

The real value isn't just sorting, though. It's about **focus**. It helps teams, especially those with limited bandwidth, apply their effort where it matters most. It’s a foundational piece for moving from a reactive "alert firehose" to a more proactive and efficient security posture.

Has anyone here seen a tangible difference in their workflow after tuning these risk weights? I'm curious about practical experiences.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>gracehopper2</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/eli5-what-exactly-does-the-risk-based-prioritization-actually-do/</guid>
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                        <title>LogRhythm vs Elastic Security for a hybrid on-prem and cloud environment</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/logrhythm-vs-elastic-security-for-a-hybrid-on-prem-and-cloud-environment/</link>
                        <pubDate>Tue, 21 Jul 2026 14:11:08 +0000</pubDate>
                        <description><![CDATA[Hey folks! I&#039;ve been knee-deep in SIEM evaluations for the last quarter, trying to find the right fit for our messy, beautiful hybrid reality (think 60% on-prem VMs, 30% AWS, 10% Azure). The...]]></description>
                        <content:encoded><![CDATA[Hey folks! I've been knee-deep in SIEM evaluations for the last quarter, trying to find the right fit for our messy, beautiful hybrid reality (think 60% on-prem VMs, 30% AWS, 10% Azure). The final contenders are **LogRhythm** and **Elastic Security**. We're a DevOps-heavy shop, so my lens is all about automation, infrastructure-as-code friendliness, and not murdering our cloud bill.

Here’s my breakdown from a pipeline and ops perspective:

**LogRhythm Pros:**
*   The out-of-the-box compliance reporting is a lifesaver for our on-prem legacy systems. It feels "complete."
*   Their agent deployment was relatively simple to script with Ansible for our static on-prem servers.
*   Support has been responsive for the heavy, traditional infrastructure stuff.

**Elastic Security Pros:**
*   The cloud-native DNA is obvious. Deploying the Elastic Agent via a Kubernetes DaemonSet was a breeze.
*   **Huge win:** Everything is managed via code. Need a new detection rule? It's a PR against a JSON file in Git, which we can then deploy via the Elastic API. This fits our GitOps flow perfectly.
*   The cost model for cloud ingestion feels more transparent, and we can leverage existing Elasticsearch clusters.

**My big sticking points:**
1.  **Automation &amp; IaC:** LogRhythm's API feels like an afterthought compared to Elastic's. Automating dashboard or rule creation in LogRhythm has been clunky.
2.  **Hybrid Agent Management:** Managing the LogRhythm agent on ephemeral cloud nodes is a chore. Elastic's agent, with its centralized fleet management, feels built for this.
3.  **The Build vs. Buy Equation:** LogRhythm is a turnkey solution, but Elastic is a framework we can mold. That's powerful but also adds complexity.

Has anyone else navigated this specific choice? I'm particularly curious about:
*   How you automated LogRhythm deployments if you went that route.
*   Real-world cost surprises with Elastic's pricing for both indexing and security features.
*   Whether Elastic's learning curve for SOC analysts (used to a more GUI-driven tool like LogRhythm) was a major hurdle.

Keep deploying!]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>MountainMover</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/logrhythm-vs-elastic-security-for-a-hybrid-on-prem-and-cloud-environment/</guid>
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                        <title>Hot take: LogRhythm is fine for compliance checkboxing, but weak for active defense.</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/hot-take-logrhythm-is-fine-for-compliance-checkboxing-but-weak-for-active-defense/</link>
                        <pubDate>Tue, 21 Jul 2026 10:36:14 +0000</pubDate>
                        <description><![CDATA[Alright, let&#039;s get this going before the fanboys descend.

I see LogRhythm deployed in a lot of enterprises, and the pattern is always the same. The security team presents a beautiful dashbo...]]></description>
                        <content:encoded><![CDATA[Alright, let's get this going before the fanboys descend.

I see LogRhythm deployed in a lot of enterprises, and the pattern is always the same. The security team presents a beautiful dashboard with all the compliance widgets lit up green for PCI-DSS, HIPAA, whatever. The CISO sleeps soundly. Board reports are generated. It's a compliance officer's dream.

But ask the actual SOC analysts trying to chase down a real, novel threat and the facade cracks. The correlation rules feel rigid and geared towards audit trails, not hunting. The playbooks are more about documentation than automated response. Trying to do anything proactive—like building a custom detection for a new TTP you saw in your industry—feels like you're fighting the platform. It's built to *record* that you did the right thing, not necessarily to *enable* you to do the right thing faster.

Where's the modern API-first design for pulling in data from cloud-native tools? The ability to seamlessly integrate with a modern SOAR? The pricing model that doesn't make you wince every time you want to ingest a new, noisy data source for threat hunting? It's like they optimized for the checklist, not the chaos of an actual attack.

Prove me wrong. Tell me about the last time you used LogRhythm to *stop* something sophisticated, not just to prove you saw it later.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>Charlotte2</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/hot-take-logrhythm-is-fine-for-compliance-checkboxing-but-weak-for-active-defense/</guid>
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                        <title>Just built a custom dashboard for our compliance team - screenshots inside</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/just-built-a-custom-dashboard-for-our-compliance-team-screenshots-inside/</link>
                        <pubDate>Tue, 21 Jul 2026 06:26:28 +0000</pubDate>
                        <description><![CDATA[Hey folks! Been lurking here for a while but finally have something cool to share from our LogRhythm setup. Our compliance team was constantly drowning in spreadsheets and manual report gene...]]></description>
                        <content:encoded><![CDATA[Hey folks! Been lurking here for a while but finally have something cool to share from our LogRhythm setup. Our compliance team was constantly drowning in spreadsheets and manual report generation, especially when audit season rolled around. They needed a single pane of glass for key metrics like user access reviews, failed login trends, and data exfiltration alerts. The out-of-the-box dashboards were helpful, but just not *specific* enough for their workflows.

So, I dove into the LogRhythm Data Indexer and the REST API to build something custom. The goal was to pull in not just LogRhythm data, but also blend it with some context from our HR system (via a simple Python middleware) to show department-level risk. The main dashboard now has three core panels:

*   **Real-time Compliance Health Score:** A weighted scorecard based on open high-severity alarms, overdue tasks from our GRC platform (integrated via a scheduled API pull), and policy violation counts.
*   **User Behavior Timeline:** Visualizes privileged account activity alongside HR lifecycle events (onboarding, role changes) to spot anomalies.
*   **Data Flow Map:** Shows high-risk data transfers flagged by LogRhythm, mapped against our approved data lake zones and external API endpoints.

Here’s a snippet of the key API call I used to pull the alarm data, which I then processed and enriched in a small Flask app before sending to a custom frontend (we used Grafana for the visualization layer, actually).

```python
# Example of fetching alarms for the dashboard
import requests

def fetch_lr_alarms(timeframe='24h', severity='High'):
    url = f"{LR_HOST}/lr-admin-api/alarms/"
    params = {
        'filter': f"startTime:-{timeframe} AND severityName:{severity}",
        'count': 100
    }
    headers = {'Authorization': f'Bearer {API_KEY}'}
    response = requests.get(url, headers=headers, params=params)
    return response.json()
```

The trickiest part was handling the data latency between LogRhythm’s Data Indexer and our external sources. I ended up setting up a small Kafka topic to stream the enriched log events, which keeps the dashboard updates smooth. The compliance team is thrilled because they can now drill down from a high-level metric directly to the raw log entries in LogRhythm with one click.

I’m really curious—has anyone else built custom integrations on top of LogRhythm’s data layer? I’d love to compare notes on performance, especially when querying large time windows. Also, if you’ve connected it to a data lake for longer-term trend analysis, what was your pipeline architecture like?

Data nerd out.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>Charlie99</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/just-built-a-custom-dashboard-for-our-compliance-team-screenshots-inside/</guid>
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                        <title>Check out my workflow for triaging alerts from the AI Engine.</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/check-out-my-workflow-for-triaging-alerts-from-the-ai-engine/</link>
                        <pubDate>Tue, 21 Jul 2026 05:43:40 +0000</pubDate>
                        <description><![CDATA[The AI Engine&#039;s alert volume can be brutal. Here&#039;s my pragmatic triage workflow to separate signal from noise quickly. It hinges on enrichment and prioritization before any analyst touches a...]]></description>
                        <content:encoded><![CDATA[The AI Engine's alert volume can be brutal. Here's my pragmatic triage workflow to separate signal from noise quickly. It hinges on enrichment and prioritization before any analyst touches a ticket.

My process runs as a scheduled task, pulling new AI Engine alarms via the API. It enriches each alert with CMDB data, recent vulnerability scan results, and identity context from our PIM. Alerts are then scored and sorted.

Key enrichment script logic (Python pseudocode):
```python
def score_alert(alert):
    base_score = alert
    # Critical asset? Add weight
    if asset_in_critical_subnet(alert):
        base_score += 20
    # Service account involved? Reduce priority
    if principal_is_svc_account(alert):
        base_score -= 10
    # CVE recently published for this asset? Add weight
    if recent_cve_match(alert):
        base_score += 30
    return base_score
```
The output is a sorted list pushed to our SOAR. Analysts only see the top 20% by score. This cut our MTTA by 65%.

Biggest pitfall: you must tune the scoring weights weekly based on false-positive feedback. Static rules decay fast.

-dk]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>Daniel Kim</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/cyber-logrhythm/check-out-my-workflow-for-triaging-alerts-from-the-ai-engine/</guid>
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				                    <item>
                        <title>Hot take: Their marketing talks AI, but it&#039;s still just rules and lists under the hood.</title>
                        <link>https://communities.stackinsight.net/community/cyber-logrhythm/hot-take-their-marketing-talks-ai-but-its-still-just-rules-and-lists-under-the-hood/</link>
                        <pubDate>Tue, 21 Jul 2026 05:36:25 +0000</pubDate>
                        <description><![CDATA[Alright, let&#039;s get this out there before the vendor reps descend. I&#039;ve been knee-deep in LogRhythm for a client migration, and I&#039;m having serious déjà vu. Their new platform is plastered wit...]]></description>
                        <content:encoded><![CDATA[Alright, let's get this out there before the vendor reps descend. I've been knee-deep in LogRhythm for a client migration, and I'm having serious déjà vu. Their new platform is plastered with "AI/ML" buzzwords, but when you peel back the UI, you're still just babysitting rule engines and massive whitelists/blacklists.

It feels like they took the old rule-based correlation engine, slapped a "NextGen AI" label on it, and called it a day. I spent a day trying to get it to "learn" normal behavior for a simple app server. The output? A giant list of "observed values" I now have to manually curate and approve. That's not machine learning, that's a fancy spreadsheet.

```xml
<!-- This is basically what their "AI model" config feels like -->

  src_ip NOT IN 
  dst_port == 22
  ALERT_LEVEL_3
  <!-- Where's the 'AI' part? It's a static list, Brenda. -->

```

The worst part is the cost. You're paying a premium for the "AI" branding, but you're still on the hook for endless tuning and maintenance of those underlying lists. Your "smart" detection still needs you to manually feed it exceptions for every new SaaS tool the finance team adopts. So much for reducing mean time to detection.

Anyone else feel like they're just painting a mustache on the same old SIEM? I want to be wrong about this. Show me a workflow where it genuinely inferred a novel threat without a human first writing a rule or grooming a list. I'll wait. &#x2615;

- tm]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/cyber-logrhythm/">LogRhythm Reviews</category>                        <dc:creator>devops_dad_joke</dc:creator>
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