What is Context? The Foundation for AI-Era Security

AI is changing what your cybersecurity team is up against. Attackers are using AI to move faster and hit harder, meaning that keeping pace with AI-driven threats is now table stakes.

At the same time, the AI you've deployed within your organization is also a threat, as attackers are actively looking for ways to exploit it. Adding to the potential liability, as we move through the early stages of the AI-era, AI infrastructure use can go sideways — from misconfiguration snafus to shadow AI vulnerabilities. 

To reveal those blind spots, organizations don’t need more data. Rather, organizations require the right context that shows what's happening, why it matters, and what to do about it — before it's too late.

What is Context?

Context is the detail behind a security event. It tells you not just that something happened, but who or what caused it, where it went, what it touched along the way, and whether it fits normal behaviors.

That detail comes from everything interacting across your environment: users, devices, applications, workloads, APIs, and machine identities, all communicating continuously. Context captures interactions at ingest speed as they cross the wire, then compares that activity against what's typical for your environment to surface, what's routine, and what isn't.

That produces decision-grade intelligence. 

The Building Blocks of Context

The Building Blocks of Context

Signal	What it tells you
Traffic flows	Who's talking to whom and how
Authentication events	Who accessed what and whether that's normal
Data movement	What's leaving your environment and where it's going
Execution activity	What code or commands are actually running
Behavioral baselines	What "normal" looks like, so deviations stand out
Forensic evidence	What happened, in order, after the fact, backed by discoverable, query-able object models

For example: a login event alone just tells you someone signed in. Context tells you it was a service account, from an unusual location, immediately followed by unusual data transfer to an external IP. That’s the difference between "nothing to see" and "this is an attack in progress".

In the AI era, this context is the new currency. Nearly 30% of AI-generated alerts already produce false positives that slow down investigations, according to this year's Global Threat Landscape Report. Context is the key to making AI more accurate, telling you which alerts are worth chasing, and which are noise that you can rule out.

What Context Tells You That Fragmented Data Can't

Other sources of log fragments: Identity shows you a user. Endpoint shows you a device. Cloud shows you an event. None of them show you how those pieces connect. Seeing how a user, a device, and an event relate to each other reveals whether you're looking at a coincidence or an attack.

Network activity is one of the most reliable threads tying that picture together, since it can’t be edited or deleted by an attacker after the fact. That makes it a critical layer of evidence within the broader context, giving you the attack chain in sequence. Without a complete, addressable evidence layer, an AI model is simply giving you a very fast opinion. 

Why AI Makes Network Context Non-Negotiable 

At AI-speed, incomplete data triggers a brutal domino effect: missed alerts, flawed decisions, stalled investigations, and compounding failures.

That’s true whether the gap shows up in detection, network operations, agentic workflows, or security consolidation. Each of these four areas demands the same foundation: complete, relational, decision-grade context.

3 Reasons Why Your SOC Needs Context

1. Detect Threats at Machine Speed

Attack breakout time has shrunk exponentially. Threat actors now move from initial access to lateral movement in minutes. Transport delay is no longer a footnote in agentic defense — it is frequently the whole result. Detection that depends on scheduled data collection, batch pipelines, or manual review can't keep up with that speed. An AI agent waiting for evidence to land in a late pipeline is reasoning about a network that no longer exists.

Context must be real-time; every session, transaction, and movement is captured at ingest speed as it happens.

2. Power Reliable Decisions in the Agentic SOC

When SOC agents reason from fragmented or delayed data, they don’t just miss threats — they become confidently wrong at scale, with permission to act. For example, in the GTG-1002 campaign, an autonomous agent carried out an estimated 80 to 90 percent of the attack lifecycle before defenders could triage a single alert. Attackers created a sequence of small, individually innocuous-looking tasks that allowed them to stealthily bypass security controls.

Activities at that rate left no time for agents reasoning from fragmented data. Without context, agents see individual endpoint events, not the coordinated chain behind them.

With context, the agentic SOC gets that cross-domain visibility, identity, endpoint, application, and network activity in one view, so a single anomaly that looks harmless alone can be seen alongside the user, device, and destination, revealing a coordinated intrusion and enabling rapid, accurate decisions.

3. Adopt AI Without Expanding Your Risk Surface

Every new AI service deployed across the enterprise creates infrastructure that existing security tools weren't built to see. Siloed toolsets can also produce contradictory views, and in such cases, neither security nor network teams see the full picture of what's running across the environment.

Context unifies views, surfacing shadow AI and every other service. Because that same context covers every service running in the environment, you can adopt new AI at the speed your business wants, without adding a new potential blind spot every time.

The Agentic SOC Alliance

This isn't just an internal operational requirement — it’s how the broader security ecosystem is structuring autonomous defense.

Building on this vision, ExtraHop CEO Greg Clark and 15 founding technology partners — spanning endpoint, network, orchestration, and AI frameworks — launched the Agentic SOC Alliance to codify an open architecture for machine-speed defense.

The Agentic SOC Alliance codifies this architecture into three distinct layers:

• Context: The operational knowledge graph mapping every device, identity, workload, and behavior via open, discoverable schemas.
• Harness: The governance runtime enforcing guardrails and audit trails around agent actions.
• Model: The reasoning layer that executes triage, investigation, and response.

Context sits at the foundation because everything downstream relies on it. When given incomplete data, agentic models still execute — they just risk making confident errors while leaving teams with no clear way to verify their logic.

Defenders win in the open and open models need open context. For a deeper look into this architecture, read ExtraHop CEO Greg Clark’s latest blog post, visit the Agentic SOC Alliance web page, and explore our eBook The Agentic SOC Blueprint. 

For expanded insight, watch our video featuring ExtraHop’s Chief Product Officer, Kanaiya Vasani.