When we introduced our next-generation platform for the AI enterprise, we focused on a simple idea: security must operate at the same speed as the environment it protects.

Since then, one thing has become clearer: In the AI enterprise, AI isn't just accelerating attackers, it is exposing the limits of how security operates. Alerts are still noisy. Investigations are still manual. Decisions are still too slow. Adding more AI features hasn't fixed that, because the problem isn't a lack of AI. It's the application of AI to the right problems.

AI in security has a problem: it's optimized for features, not outcomes

In the AI enterprise, most security platforms use AI to improve individual capabilities:

• Better anomaly detection

• Fewer alerts

• Faster signal generation

But security teams don't measure success in features. They measure it in outcomes. SecOps teams are asking:

• How quickly can we detect an attack?

• How fast can we understand what's happening?

• How quickly can we respond and contain it?

At Vectra AI, our approach is to correlate signals across fundamentally disparate surfaces. We don't apply AI to features. We apply it across the lifecycle of visibility, detection, correlation, and response to reduce SecOps burden and improve real operational metrics.

Visibility: proactive security requires observability at the right depth

You can't reduce risk you don't fully understand. In the AI enterprise, visibility isn't about collecting more data, it's about capturing the right data with enough fidelity to expose attacker behavior early. In these environments, identities, including AI agents, outnumber humans, an activity never stops. Attacks don't live in one domain. They move across network, identity, SaaS, cloud, and AI infrastructure.

Most tools see fragments of this activity. Vectra AI approaches this differently. We anchor on telemetry from the network (packets and flows) as ground truth. We augment this with identity and cloud telemetry from platforms such as Entra ID, M365, AWS and Azure. This allows us to:

• Reconstruct behaviors across domains, not just within a single surface

• Correlate identities, hosts, and services into a unified narrative

• Maintain visibility into east-west and ephemeral traffic

As AI adoption grows, this becomes even more critical. AI agents are now first-class identities — operating across the same hybrid environment and introducing new attack paths. Using network and identity telemetry, Vectra AI provides visibility into:

• Data movement to AI service providers

• Use of agentic applications and frameworks

• Unsanctioned tools (e.g., open-source agent frameworks)

• Activity across platforms like Copilot Studio, AWS Bedrock and Azure AI

This isn’t log aggregation. It’s behavioral observability across the hybrid footprint, enabling teams to close exposure gaps earlier, proactively hunt threats, and improve overall security posture.

Detection: using the right methodology for the right threat use-case

Proactive defense within the AI enterprise starts with behaviors, not alerts.  

A single AI approach can’t solve every security challenge. The no free lunch theorem tells us no single model performs best across all problems. Attacker behaviors are no exception.

• Command-and-control traffic behaves like time-series data

• Identity privilege abuse behaves like multi-dimensional baselines

• Lateral movement across multi-cloud behaves like relationship graphs

Using the same model for all of these doesn’t simplify security. It creates noise.

Vectra AI takes a security-led approach to AI: start with the attacker methods (not the data), identify the behaviors that define them and then apply the right models for high-fidelity detection.

What this looks like in practice:

• Command and Control: We use recurrent neural networks (LSTMs) to model the shape of communication over time, detecting control channels even when encrypted or obfuscated.

• Identity privilege abuse: We model real-world behavior across accounts, hosts, and services to detect misuse of access, not just deviations from static baselines.

• Lateral movement: We use relationship-based modeling to track how attackers move between surfaces.

Attackers can change tools. They can change infrastructure. But they can’t easily change behavior.

Most AI-driven tools today are built to find anomalies. We build AI to detect attacker methods. The result is higher-fidelity detection with less noise:

• Over 90% MITRE ATT&CK coverage  

• Most references of any vendor in MITRE Defend  

• Fewer false positives and more actionable signal

Correlation: the importance of connecting the dots

In the AI enterprise, attacks don’t occur as single events. They unfold as connected behaviors across surfaces that most tools surface as isolated alerts. Attackers execute coordinated actions that only make sense when viewed together.

The Vectra AI platform is built to connect the dots. Using AI-driven stitching, we link behaviors across network activity, identity telemetry, cloud operations and SaaS interactions into a single unified narrative. This is powered by a proprietary translation layer that brings together fundamentally different forms of telemetry - packets, logs, and configuration data into a common behavioral model.  

The result is fewer, higher-confidence alerts that reflect real attacker activity. This enables:

• Campaign-level visibility: Surface full attacker campaigns, not just disconnected detections

• Behavior-based prioritization: Score activity based on progression and risk, not static severity

• Identity-centric attribution: Track attacks to identities and hosts, not transient indicators like IPs

This is the difference between seeing individual events and understanding attacks. Understanding behaviors is only half the problem. The real challenge is acting on that insight, fast enough to stop it. 

Response: accelerated response means eliminating analyst latency

Detection speed isn't the bottleneck anymore. Decision speed is.

SecOps teams today are inundated with signal but still lack the context to act fast enough. They’re still:

• Manually triaging alerts

• Correlating signals across tools

• Writing queries to understand what’s happening

In the AI enterprise, where attackers operate at machine speed, attacks can progress in minutes. If response still depends on manual workflows, it is already too slow. At Vectra AI, we focus on eliminating decision latency. Not just detection latency.

What this looks like in practice:

• AI-driven prioritization: Behaviors are automatically correlated and scored based on attack progression, so teams focus on what actually matters

• Pre-built investigative workflows: One-click investigations surface full context across network, identity, and cloud—without manual stitching.

• AI-assisted analysis: Analysts can query telemetry using natural language and get immediate, contextual answers and recommended next steps.

The result is a fundamental shift in how SecOps teams operate:

• Over 45 minutes saved per investigation

• Significant reduction in alert volume and noise. In environments with 1000+ identities, customers observed fewer than 10 require investigation

This isn't just faster response. It's removing the friction between detection and action so teams can contain attacks while they're still in motion. Attackers are already operating at machine speed. Security teams don't need more alerts. They need the ability to decide and act faster.

A fundamentally different approach to cyber resilience

Defending the AI enterprise isn’t about adding more tools or more AI features. It requires a shift in how security operates:

• From data-first to behavior-first

• From alerts to outcomes  

• From detection speed to decision speed  

Most approaches stop at generating signals. But signals don't stop attacks. Understanding and acting on them does.

That's what it means to use AI to protect the AI enterprise. And more importantly, that’s what it takes to reduce the burden on the teams defending it.  

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