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Adversaries are using AI to reach breakout faster than most security teams can react.

Predictive security operations is the only way to close this gap.

To achieve predictive security operations, organizations need to embrace agentic systems—objective-driven agentic AI architectures that function like a governing brain across security operations. Unlike task-based AI agents, agentic systems operate across the full incident lifecycle, continuously correlating signals, applying learned expertise, and adapting to changes in real time.

But agentic systems can only provide the right predictions if they’re given the right inputs. In this guide, you’ll learn how to build a clean foundation of data, how AI uses this data to predict threats, and how agentic systems can orchestrate preventive actions based on those predictions.

The 5 Phases to Reaching Predictive Security Operations


Phase 1: Aggregate Your Threat Intelligence


Fragmented threat data is noise. AI can't correlate what isn't connected, contextualize what's scattered, or automate decisions on incomplete information.

But aggregation isn't just consolidation. It includes collecting intelligence from disparate sources in ways that reveal threat patterns across your entire security infrastructure. This makes aggregation the critical first step for predictive operations, enabling real-time detection across architectures, tools, and defenses.

How to Approach It


Three things make aggregation work:

1. Pull indicators from everywhere and normalize into one consistent model.

Your tools create threat indicators written in different syntaxes. Normalize this data into a single language so every system downstream can understand and use it.

2. Eliminate duplicates and enrich signals as they arrive.

An alert may fire across multiple tools, and each tool provides different information. Merge these details to dedupe alerts. Then, use automation to add relevant IOCs and other context to a unified alert: what threat actors are connected, associated techniques they use, attack patterns, severity indicators, etc.

3. Extract and distribute IOCs across your security technologies in real time.

Once alerts are normalized and enriched, push IOCs to your endpoint detection, firewalls, DNS filters, and email gateways for immediate blocking and detection tuning.


Phase 2: Map Threats to Your Unique Environment


Without environmental context, threat intelligence is generic noise that results in irrelevant threat hunts and missed critical exposures. This phase makes the connection between threat intelligence and your assets and identities so AI can quickly surface the most relevant threats and exposures.

How to Approach It

To map threats to your environment:

1. Inventory your systems and their connections.

Know not just what systems you own, but what they connect to, what data they process, and which business functions depend on them. This asset context becomes your reference layer for all threat mapping.

2. Know where sensitive access lives and how permissions are distributed.

Identity has become the perimeter. When you map threats to your identity systems, you see which compromises could create exploitable pathways and which are blocked by proper permission models.

3. Prioritize threats according to your industry and environment.

Combine your unique business telemetry—incident response data, identified exposures, and attack surface—with industry-specific threat intelligence to narrow your focus to the most relevant threats.



Phase 3: Extend Visibility Beyond Your Perimeter


Attackers operate where SOCs lack visibility. Before attackers reach your perimeter, they've already identified targets, chosen attack vectors, registered malicious domain infrastructures, and gathered credentials for initial access. Predictive defense requires identifying these signals before they become attacks.

How to Approach It

To extend visibility beyond your perimeter:

1. Monitor where your organization surfaces on the open, deep, and dark web.

Your team’s credentials, domains, infrastructure data, and brand assets show up in vendor breach mentions, threat actor discussions, and credential markets. Attackers use this intelligence to target you before you know you're exposed. By enabling continuous external monitoring, exposures and vulnerabilities are detected before they're exploited.

2. Track reconnaissance activity targeting your infrastructure.

Scanning, probing, and DNS reconnaissance activity often precedes compromise attempts and reveals where attackers are focusing their attention. When you can see reconnaissance happening, you can see attacks form.

3. Understand how exposed data are being weaponized in active campaigns.

When and how your data appears in active attacks reveals response urgency and priority: An attack using a credential from a paste site months ago looks different from one using a credential compromised through a recent incident.



Phase 4: Make Predictions with Agentic AI


You now have three clean inputs that an agentic system can use to predict threats: unified threat intelligence, contextualized environmental data, and extended visibility.

Now AI can look at specific telemetry to identify where attackers are most likely to break in and how they might cause the greatest harm to your environment.

How to Approach It


To make its predictions, AI synthesizes three layers of information:

1. Internal trends.

These are detection gaps within your environment, threat hunt findings and true-positive alerts from your incident response or exposures you've already identified. This reveals patterns of what's actually happening inside your operations.

2. Business context.

This includes your industry, technology stack, asset criticality, VIP users, and contextual information unique to your business —grounding predictions in your specific environment.

3. External landscape.

For example, threat actors targeting your industry and current campaigns, impersonating domains targeting your brand, and credential threats against your privileged users.


Statistical Analysis Makes the Prediction

By combining all this information, AI can perform statistical analysis to forecast threat likelihood and impact and help your team prioritize response on threats that matter most to your business.

Phase 5: Take Action with Agentic Systems


The fifth and final phase transforms predictions into autonomous, coordinated response with agentic systems that function as an intelligent extension of your team, executing complex responses, learning from outcomes, and improving continuously.

How to Approach It


Here’s what orchestrated response with an agentic system should look like:

1. Command Complex Actions in Natural Language.

These are detection gaps within your environment, threat hunt findings and true-positive alerts from your incident response or exposures you've already identified. This reveals patterns of what's actually happening inside your operations.

2. Respond Autonomously Based on Learned Experience.

This includes your industry, technology stack, asset criticality, VIP users, and contextual information unique to your business —grounding predictions in your specific environment.

3. Orchestrate Specialized Agents Across Your SOC.

Agentic systems can also orchestrate collaborative teams of specialized agents that share context and adapt to each other’s findings. For example, under the direction of an agentic system, an AI SOC agent specializing in threat intel can identify a new threat and automatically collaborate with a detection engineering agent to build and deploy a new detection. Simultaneously, the agentic system can task a threat hunting agent to search for any historical signs of compromise.


ReliaQuest GreyMatter Makes Predictive SecOps Operational

Predicting threats and preventing attacks across a complex ecosystem requires a platform built for orchestration at enterprise scale. With hundreds of bi-directional integrations, ReliaQuest’s GreyMatter agentic AI security operations platform serves as this connective layer: ingesting disparate threat sources into standardized schemas, enabling bidirectional push/pull across tools, mapping threats to your environment automatically, and orchestrating agentic AI systems to correlate and respond at speed and scale—learning from collective industry patterns to continuously strengthen your threat model.

GreyMatter operationalizes these five phases to drive your team from a reactive to a predictive AI SOC. Detection shifts from hours to seconds. Response shifts from manual triage to autonomous containment. And your analysts regain the time for the investigation and threat hunting that drives real defensive maturity.