How AI is Connecting Analysis, Threat Hunting and Cloud Investigations

Security, fraud, and cybersecurity teams face a growing challenge: protecting increasingly complex environments shaped by cloud adoption, AI, and IoT, while threats evolve faster than ever. Bad actors are also leveraging AI to scale and accelerate their attacks.

AI is no longer optional,  it has become essential. When used correctly, AI can improve security posture and operational efficiency, not by replacing people, but by connecting critical security functions and breaking down silos.

The Security Triangle: Three Functions, One Mission

Modern security teams, tend to rely on the same three functions:

1.      Data Analysts focus on understanding what’s happening right now. When systems flag suspicious activity, they determine what it means, how serious it is, where it likely originated and apply detection and disruption rules

2.      Threat intelligence and threat hunting are proactive teams that work together to reduce detection time. Threat intelligence gathers and analyses data about adversaries, campaigns, and indicators to produce context and hypotheses. Threat hunters use those hypotheses, combined with internal telemetry, to proactively search for stealthy activity that automated tools may miss, validate indicators of compromise (IOCs), and feed findings back to the data analysts to build detection and response processes

3.      Cloud Investigations. Cloud environments introduce unique challenges. When something goes wrong, investigators must reconstruct activity across multiple services, accounts, and regions often with incomplete, fragmented, or scattered data.

The Four Musketeers: When AI is part of the Equation

AI links the three security pillars to create a more intelligent and responsive security posture. Here’s what that looks like in action:

1.      Reducing Noise

Security systems generate enormous amounts of data logs, alerts, network traffic patterns, user behaviours. A single organization might see thousands of security alerts every day. AI excels at analysing these data points and remove noise by recognizing patterns humans could take longer to identify, meanwhile prioritizing the urgent ones.

 That said, human analysts remain essential. Humans understand business context, recognize what’s “normal” for specific teams, and identify attacks that don’t follow known patterns. AI does the initial sorting; people apply judgment.

2.      Connecting the Dots

One task that AI excel on is in data correlation. This is especially valuable in cloud environments, where activity happens in multiple platforms, services, and identities.

 3.      Learning What "Normal" Looks Like

Over time, AI systems can build a benchmarks to learn what a normal behaviour looks like. It learns when teams typically access resources, how data usually flows, and what authentication patterns look like and when something deviates to flag for human review.

This capability is particularly useful for threat hunting, where the goal is to find breaches that haven’t triggered traditional controls.

Use case example: How It All Works Together

Here’s a short use case scenario that shows how AI, analysts, threat hunting, and investigation combine to detect, investigate, and learn from a cloud security incident. Let's walk through a realistic scenario to see how AI integrates these three functions:

Step 1: The Initial Signal

An AI system detects unusual API calls to cloud storage. The behavior isn’t severe enough to trigger an alert, but it’s statistically abnormal, so the activity is escalated to the security team and analyst requesting a root‑cause investigation.

 Step 2: The Hunt Begins

Findings are shared with threat intelligence, hunting, and investigation teams. With AI assistance, analysts automatically gather related data, users, locations, timestamps, authentication attempts, and associated cloud services and begin building a unified timeline.

Step 3: The Investigation

The team confirms the activity is suspicious. AI‑generated investigative leads help map scope and impact: which cloud resources were accessed, what data was touched, and whether any data may have been exfiltrated. Instead of manually putting together logs from multiple services, analysts receive a consolidated view of events and timelines. What would normally take a day of log review is reduced to hours, while human oversight remains central.

Step 4: The Four Musketeers

Using insights from threat analysis and AI correlation, investigators and threat hunters build an investigation plan. Their goal is to determine whether the activity reflects a threat actor, an operational issue, or a process weakness and identify the potential bad actor to refer to law enforcement. Together they model possible attacker paths, test assumptions, and identify gaps in controls or visibility. Technical signals are translated into risk understanding, attribution and leads.

Step 5: Response and Learning

Threat analysts document the full attack chain and confirm the root cause: a cloud misconfiguration that wasn’t being monitored closely enough.

An intelligence report, build with AI assistance and containing validated IOCs, is shared across team customers. That insight feeds back into updated controls, detection logic, and monitoring coverage. AI systems also incorporate the observed patterns, improving their ability to flag similar behaviour in the future.

Step 6: Outcome 

The result is not just resolution but a stronger security posture going forward. In this example, AI doesn’t replace security professionals, it amplifies their abilities.

My final thoughts

The threats aren't getting simpler, and cloud environments aren't getting less complex. But with AI connecting the dots between analysis, hunting, and investigation, security teams can build capabilities to match at scale modern threats. This is where I believe we are moving towards:

• AI conducts preliminary investigations and presents findings to human analysts

• Threat hunting becomes increasingly predictive, with AI suggesting where to look before incidents occur

• Cloud security becomes more proactive, with AI identifying misconfigurations and vulnerabilities before they're exploited

• Response times shrink from days to hours or even minutes

The key is viewing AI as an enabler of better security outcomes, not a magic solution. The most effective security operations will be those that thoughtfully integrate AI capabilities with human expertise, creating a synergy that’s more powerful than either alone by:

-          Creating feedback loops. Findings from threat hunting and investigations should continuously feed back into AI models and detection logic.

-          Invest in your team. Leaders need to understand both the capabilities and limitations of AI, while also supporting its effective use to deliver results. Training is key. It opens the door to new ideas and better ways to leverage AI for the benefit of the entire team.

The final question isn’t whether to integrate AI into your security operations it’s how quickly you can do so effectively, while keeping the human element at the centre of your strategy.