Detecting Threats Before They Happen with Peris.ai’s Brahma IRP

For years, cybersecurity strategies have primarily focused on detecting and responding to threats after they occur. Organizations deploy SIEMs, EDRs, and firewalls that generate alerts once malicious activity is underway. But in today’s threat landscape—riddled with zero-day exploits, lateral movement, AI-generated malware, and stealthy reconnaissance—waiting for an alert is already too late.

“You can’t contain what you didn’t see coming.”

Security leaders are waking up to a new reality: the future of cybersecurity is predictive. It's not enough to monitor events and respond. Enterprises need to anticipate and neutralize threats before they become incidents.

This article explores:

• The limitations of reactive security
• The real-world impact of detection delays
• Why traditional tools fall short of early detection
• How Peris.ai’s Brahma IRP helps organizations shift from reactive to proactive defense
• And how to implement predictive detection in your enterprise without overwhelming your team

The Cost of Delayed Detection

According to IBM’s 2024 Cost of a Data Breach Report, the global average cost of a data breach has increased to $4.88 million, marking a 10% rise from the previous year. The average time to identify a breach remains at 204 days, with an additional 73 days to contain it, totaling a breach lifecycle of 277 days.

Key pain points for security teams include:

• Slow Mean Time to Detect (MTTD)
• Manual triage and alert correlation
• Lack of threat context
• Siloed visibility across endpoints, networks, and clouds
• Inability to anticipate emerging threats

Attackers now operate faster than ever, often exploiting vulnerabilities within hours of their disclosure. Once inside, they move laterally, escalate privileges, and often go undetected for months.

The takeaway: If you’re only detecting threats once they’re active, you’ve already lost half the battle.

Why Most Security Architectures Remain Reactive

Traditional security operations centers (SOCs) rely on layers of detection tools—SIEMs, IDS/IPS, antivirus, EDRs. These tools typically:

• Generate alerts after malicious activity
• Depend on signatures or predefined rules
• Require human correlation for triage
• Lack business or threat context

The result?

• Overwhelming alert volumes (most of them irrelevant)
• Reactive incident response
• Inability to spot “quiet” precursors like recon scans or misconfigurations
• Analyst burnout due to sifting through irrelevant alerts while genuine threats go unnoticed

This is where the shift to predictive threat detection becomes urgent.

What Predictive Threat Detection Really Means

Predictive detection isn’t magic—it’s about combining visibility, intelligence, and automation to surface threats before they manifest as incidents.

Components of predictive security:

👁️ Visibility

• Deep telemetry across endpoint, network, and cloud

🧠 Threat Intelligence

• Contextual understanding of attacker behavior

🔁 Automation

• Real-time correlation, triage, and playbook execution

🧹 Integration

• Unified workflows across all data sources

📊 Continuous Learning

• Adaptive playbooks based on threat evolution

Brahma IRP leverages all these pillars to deliver truly proactive cybersecurity.

Introducing Brahma IRP: The Intelligent Nerve Center of Cyber Defense

Brahma IRP is the Incident Response Platform at the core of the Peris.ai ecosystem. But it’s far more than a response tool—it’s a predictive detection and decision-making engine built for modern threats.

Core Components:

• Brahma Fusion (Automation & Orchestration) Intelligent AI agents analyze incoming data, launch playbooks, and reduce detection time from hours to minutes.
• INDRA (Cyber Threat Intelligence) Enriches alerts with threat actor tactics, CVE exploitability, campaign data, and MITRE ATT&CK mapping.
• Peris.ai NVM (Network Visibility Monitoring) Detects anomalous traffic, lateral movement, and unknown devices—even in encrypted traffic streams.
• Peris.ai EDR Provides endpoint-level telemetry, behavior analytics, and process-level visibility.
• BimaRed (Attack Surface Management) Identifies exposed assets and risks before attackers do—feeding early warnings into Brahma IRP.

Together, these systems create a 360° view of your environment—one that not only sees everything, but understands what to do with what it sees.

How Brahma IRP Detects Threats Before They Happen

Let’s explore how Peris.ai’s Brahma IRP transforms SOC operations from reactive to predictive through three critical capabilities:

A. Agentic AI for Proactive Triage

Traditional triage:

• Requires analysts to manually pivot across SIEM, EDR, and CTI tools
• Involves hours of log analysis, query writing, and cross-referencing
• Is slow, inconsistent, and error-prone

With Brahma Fusion:

• AI agents ingest alerts from multiple sources (e.g., failed login, DNS anomalies)
• Automatically correlate telemetry across endpoints, network, and cloud
• Cross-reference findings with threat intelligence from INDRA
• Determine severity based on business context, exploitability, and asset criticality
• Trigger containment or escalation playbooks automatically

The result: Level 1 and Level 2 analyst duties are performed in seconds, not hours.

B. Real-Time Visibility Across Every Layer

Brahma IRP connects data from:

• EDR (endpoint behavior)
• NVM (network traffic)
• Cloud workloads
• Threat intelligence feeds
• Internet-exposed assets via BimaRed

This full-spectrum telemetry allows IRP to:

• Detect lateral movement patterns
• Monitor for unusual connections or traffic spikes
• Flag new shadow assets as soon as they appear
• Correlate emerging CVEs with your actual assets
• Spot early-stage TTPs like phishing reconnaissance or domain fronting

This pre-breach visibility turns potential indicators into actionable intelligence.

C. Threat Context That Drives Priority

A traditional SIEM might show a port scan. IRP shows that:

• It was from an IP tied to TA505, a known ransomware gang
• It targeted a system with a critical unpatched CVE
• The asset is tied to your HR payroll server
• The exploit has a 90% EPSS score and is trending in hacker forums

That’s not just a scan—that’s an imminent breach.

This is what context-aware detection looks like.

Key Benefits of Brahma IRP in Proactive Detection

🧠 Triage time cut by 70%

• Alerts are processed and prioritized by AI

🔍 Reduced false positives

• Alerts enriched with threat context

🛡️ Breach containment before exfiltration

• Threats intercepted at pre-execution phase

📉 Analyst burnout drops

• Repetitive tasks handled by automation

📊 Compliance and audit alignment

• Full lifecycle case management and reporting

Integrating IRP Into Your Existing Security Stack

You don’t have to rip and replace.

Brahma IRP is built to integrate with:

• Existing SIEMs (e.g., Splunk, QRadar, Elastic)
• Endpoint tools (via agent or API)
• Ticketing platforms (e.g., ServiceNow, Jira)
• Threat feeds and internal vulnerability scanners
• Firewall and NDR vendors

This ensures gradual adoption, fast ROI, and minimal disruption.

KPIs to Watch After Deploying Brahma IRP

MTTD (Mean Time to Detect)

• Before IRP: 6–12 hours
• With Brahma IRP: <15 minutes

MTTR (Mean Time to Respond)

• Before IRP: 1–3 days
• With Brahma IRP: <2 hours

Analyst Workload (Manual Triage)

• Before IRP: 80% of time
• With Brahma IRP: 30% or less

Contextualized Alerts

• Before IRP: <10%
• With Brahma IRP: 80%+

Breach Dwell Time

• Before IRP: Weeks
• With Brahma IRP: Measured in minutes

Getting Started: Shifting to Predictive Security

Step 1: Visibility Audit

Identify blindspots across endpoint, network, and cloud. Use BimaRed and NVM to map your environment.

Step 2: Integrate Threat Intelligence

Feed Peris.ai’s INDRA into your SOC processes for real-time TTP matching.

Step 3: Automate Triage

Replace manual playbooks with Brahma Fusion’s AI-generated sequences for detection, correlation, and escalation.

Step 4: Establish Metrics

Track pre- and post-IRP MTTD, alert volumes, false positives, and team workload.

Step 5: Continuously Improve

Use Brahma IRP’s feedback loop to refine detections, suppress noise, and surface what really matters.

Conclusion: See Before It Strikes

In cybersecurity, seconds matter. The difference between catching a threat before execution and after a breach can mean:

• Millions in losses
• Days of downtime
• Permanent reputational damage

Peris.ai’s Brahma IRP isn’t just a response platform—it’s your early warning system. It helps you:

• See beyond alerts
• Understand adversary intent
• Automate intelligent action
• And most critically—detect threats before they happen

🚨 Ready to take your detection capabilities from reactive to predictive? Visit https://peris.ai to learn how Brahma IRP can transform your SOC into a proactive defense hub.