From Noise to Signal: Optimising Microsoft E5

Overlapping tools create blind spots. Here’s how to consolidate them.

Microsoft E5 can be an excellent security investment, but without targeted configuration, integration, and continual threat alignment, its value remains untapped. Over the years, building out custom SOC, MDR, and MXDR services has shown us how to move from licenced capability to measurably reduced response times, cleaner telemetry, and security teams who trust the picture in front of them.

Microsoft’s E5 licence is marketed as an all-in-one enterprise-grade security solution, bundling together a broad set of capabilities that span endpoint protection, identity governance, compliance, and SIEM. For security and IT leaders, the promise is appealing; consolidate toolsets, simplify procurement, and lower vendor sprawl.

But we know from countless implementations that customers typically activate only a fraction of the E5 stack’s available security. This is consistently reflected in wider reporting, with studies from Gartner and Forrester showing that E5 customers regularly use less than 40% of its core features, and fewer still using the proactive controls in Defender and Sentinel.

E5’s tools are powerful but not prescriptive. By default, most features are under configured or disabled, overly noisy due to generic rule sets, and disconnected across security and operational silos.

This leads to three key challenges:

• Excessive alert volume and false positives – Security teams become overwhelmed, leading to missed detections due to ‘alert fatigue’.
• Cost creep due to uncontrolled log ingestion – Microsoft Sentinel’s costs scale rapidly if telemetry isn’t analysed and optimised.
• Reactive rather than proactive security – Tools don’t reflect real threats or attacker behaviours without detection engineering.

So, what can be done to maximise the value of Microsoft E5 through meaningful risk reduction and proactive threat detection?

• Customise configuration to your threat profile – Leverage adversary simulations to enumerate and validate all potential or traversable attack paths within your environment. You should also ensure Sentinel rules are engineered based on observed techniques, not generic event types.
• Prioritise telemetry – Apply a threat-value framework to determine what to log and ingest. You can also reduce ingestion costs by focusing on only high-fidelity telemetry with security value.
• Implement automation for real-time defence – Defender and Entra ID controls are configured to auto-contain threats. You should also orchestrate response activity using custom logic apps and security playbooks.
• Run detection engineering sprints – Every quarter, review alert efficacy, tune logic, and retire obsolete rules. It’s a good idea to align detection coverage to the MITRE ATT&CK framework as this helps to visualise progress.
• Close the visibility gap – Integrate with Entra ID, Exchange, Defender for Identity, and Intune to unify telemetry. Ensure lateral movement attempts, privilege escalations, and anomalous behaviours are captured and correlated.

Example: Ransomware Recovery

When being called in post-compromise to support a professional services client impacted by a MOVEit-style breach, we helped to reduce incident dwell time cut from, 14 days to under 3 hours. This was achieved through detection tuning, MITRE-aligned control maturity reports, and costed risk reduction based on avoided downtime and mitigated regulatory exposure. JUMPSEC achieved this by prioritising the following activities:

• Rebuilding detection logic based on MITRE ATT&CK:
  • Fully mapped likely attack chains starting from external web compromise to privileged identity abuse.
  • Built custom Sentinel rules targeting lateral movement (T1078: Valid Accounts, T1021: Remote Services, T1550: Use of Web Credentials, T1071.001: Web Protocol Exfiltration)
• Using KQL to link logs across silos:
  • Correlated WAF traffic > Azure App Service logs > Defender Endpoint signals > Entra ID logs.
  • Applied custom UEBA logic within Defender for Identity to highlight privilege misuse across service accounts.
• Tuning Defender for Cloud Apps:
  • MCAS policies adapted to detect burst file downloads, impossible travel anomalies, unexpected third-party API access post-compromise
• Enriched Identity Visibility with Purview:
  • Linked sensitive data locations to impacted identities.
  • Enabled insider risk signals for policy violations based on unusual export patterns.
• Automated Containment via Conditional Access:
  • Pre-staged Conditional Access policies instantly restricted infected service principals.
  • Removed token grants without waiting for SIEM playbook response.

The bottom line

Irrespective of your approach, the following core principals apply to E5 maximisation:

• Data volume is not the problem, correlation is.
• Default policies catch noise. Custom detections catch attacks.
• Without mapping threat models into your detection layer, your visibility is theoretical.
• E5 has power. But only if someone tunes it to your risks.

Microsoft E5 represents a strategic investment, but only if you invest in its operation, not just its procurement. When tuned and aligned with threat behaviour, Microsoft security tools deliver cost-effective, proactive cyber defence. Without this effort, you’re relying on a generic implementation rather than building security fit for your unique organisation.

How to turn Microsoft’s licence from shelfware into real security value

IT leaders tell me the same story repeatedly. They’ve built large, sometimes expensive, security stacks, but they still don’t fully trust the picture that’s painted. Dozens of tools are running across the estate: separate agents, standalone scanners, multiple SIEMs, and identity providers layered on top of Microsoft’s native stack. Despite this, gaps remain.

When running purple teams or defensive consultancy, we often find redundant technology performing overlapping functions but not integrating well. Licensing costs rise, the operational burden increases, and most critically, response times lag while adversaries are executing swift, undetected breaches.

Events like MOVEit and CitrixBleed showed how fast high-impact vulnerabilities can be exploited. But Log4Shell remains one of the clearest examples of how fragmented tooling and visibility gaps can leave organisations exposed.

The Problem Laid Bare

When Log4Shell hit, concern spread quickly across IT and security teams. Many organisations struggled to identify vulnerable workloads because assets were spread across multiple platforms and operating models. Cloud, on-prem, legacy, and containerised workloads were all part of the mix.

In one such organisation, Azure App Services, AKS clusters, legacy VMs, and VMware workloads coexisted with five different vulnerability scanners. Microsoft Defender for Endpoint overlapped with a legacy EDR agent. Recommendations from Defender for Cloud conflicted with those from a third-party cloud security tool. Their SIEM estate spanned Sentinel and Splunk, each ingesting different log sets. The lack of coherence was the predominant contributor to the problem.

Rethink the Stack

Solving the complexity problem doesn’t begin with ripping out tools or launching new and expensive procurement rounds. It starts by shifting how you think about your architecture.

Instead of asking “What else do we need?”,  the better question is “What’s already here that we haven’t activated properly?” or even “where can we work smarter to extract more value?”

Many organisations treat E5 features as checkbox tools rather than as a platform to build on. Defender for Endpoint can provide far more than malware protection. Defender Vulnerability Management is more than a CVE reporting feed. Sentinel is not simply a log collector, and Entra ID is more than a user directory.

A more effective approach is to treat the E5 stack as a platform, not a bundle. When you do, several opportunities appear:

• Consolidation becomes risk reduction, Instead of five tools scanning five different sets of assets, a single platform provides unified, context-rich insight.
• Detection becomes behaviour-led. Instead of chasing indicators from threat intel feeds, tune Sentinel to spot tactics attackers actually use against your environment.
• Automation becomes targeted. Conditional Access and Logic Apps allow you to pre-empt threats, not just document them.

You start to see security as a continuous system, not a stack of disconnected tools.

This mindset shift changes what your security team focuses on. Rather than managing alerts across six portals, they manage outcomes from a single view. Rather than chasing more logs, they investigate anomalies that have been enriched with identity, location, and behavioural context.

They can see clearly now…

The client facing Log4Shell challenges already had the licences, tools, and telemetry, but no unified answer to “Where are we exposed?”

We worked with them to consolidate duplicate tools, retire what was redundant, and fully enable the E5 capabilities they already owned.

• Defender Vulnerability Management became the single source of exposure data, removing the confusion caused by multiple scanners.
• App Service and AKS telemetry was routed into Sentinel through targeted log collection, enriched with custom KQL rules designed to detect Log4Shell-style payloads in real time.
• Custom detection logic was written in-house, enabling them to parse inbound HTTP headers, inspect user agents, use regex matching for obfuscated JNDI strings, and map anomalous connections to MITRE ATT&CK techniques.
• Access control was restructured by reviewing legacy admin roles, moving elevated access to just-in-time workflows via Entra ID Privileged Identity Management, and enforcing Conditional Access policies based on risk, device posture, and session context.

How to cut alert fatigue and build detections that actually matter

In many organisations, the security operations centre (SOC) is overwhelmed. The volume of alerts coming from tools like Sentinel, Defender for Endpoint, and Cloud Apps is high, and growing. Spending more time triaging noise than they are stopping real threats, does this sound familiar?

This isn’t about analyst headcount or tool choice. It’s about architecture. If alerts lack context, if rules aren’t aligned to threat models, and if telemetry isn’t prioritised for security value, then teams fall into the trap of reactive operations. Detections are missed, alerts are ignored, and fatigue sets in.

From Logs to Learning: A Better Detection Strategy

The goal should be to build detection that learns, not logs that shout. To do this:

• Use adversary simulation to inform detection logic based on attacker behaviours, not just CVEs.
• Enrich alerts using KQL to bring in user, identity, device, and behavioural context.
• Consolidate and deduplicate alerts using automated playbooks.
• Shift your engineering focus to building correlation logic, not just ingesting more data.

Identity-based attacks are quiet, fast, and common.

According to Microsoft’s 2024 Cyber Signals Report, over 80% of breaches involve compromised credentials. Not malware. Not exotic exploits. Just attackers logging in as legitimate users, often using techniques that bypass endpoint protection entirely.

This shift means traditional indicators, antivirus hits, signature detections, suspicious processes, are no longer reliable early warning signs. Instead, the signals of compromise hide in identity logs, user behaviour, data access patterns, and cloud resource manipulation. Unfortunately, these are the very signals most teams aren’t equipped to monitor effectively.

Why Alert Volume ≠ Protection

In one Microsoft 365 E5 client we recently supported, Sentinel was generating nearly 10,000 alerts per week. Less than 2% were ever reviewed. Most were low or medium severity, generated by default rules, and rarely linked to specific threat behaviours.

Defender for Endpoint was active across a high proportion of devices in the estate and, due to the scale, alerts were treated in isolation. They were missing a trick; Defender for Cloud Apps (MCAS) produced policy violations , but no one was tracking whether they correlated with risky login patterns. Entra ID identity protection was running but not enforcing risk-based Conditional Access.

From a tooling perspective, everything looked fine. But when we tested detection through purple teaming, we walked through three stages of an attack path, initial credential theft, privilege escalation, and sensitive data access, without triggering a single high-confidence alert. It wasn’t a tooling failure. It was a tuning failure.

Shift from activity monitoring to threat detection

The problem wasn’t the quantity of alerts, or even the rules themselves. It was the lack of behavioural linkage.

Modern attacks don’t trigger one big alarm. They trigger a series of weak signals, failed logins, unusual admin role assignment, cloud configuration change, endpoint process injection, that only make sense when viewed together. The key is joining these dots.

This means going beyond default analytics and building detection logic that understands attacker behaviour. It means linking Defender for Endpoint to Sentinel, so endpoint telemetry feeds broader correlation rules. It means using Entra ID sign-in logs to enrich every alert with identity context. It means tracking session tokens, OAuth grants, and risky sign-ins in real time, not after the fact.

Most importantly, it means treating security alerts not as isolated notifications, but as pieces of a wider attack story that you need to reconstruct quickly.

A Real-World Example

A purple team simulation for a financial services organisation emulates a common credential-based attack.

Using legitimate credentials for a contractor account, log in is from a new IP range and accessed is gained to SharePoint sites that contained sensitive commercial data. A browser session is used to authenticate, mimicked normal working hours, and content is exfiltrated over HTTPS.

Defender for Cloud Apps flags a location anomaly. Entra ID tags the sign-in as medium risk. But neither alert is escalated or triaged. Sentinel hasn’t fired a correlation rule, because those signals aren’t connected.

After a new detection framework, those same behaviours trigger a three-stage response:

• Entra ID tags the login.
• Cloud Apps flags data movement.
• Sentinel correlates the pattern using custom KQL analytics.

That combination produces a high-severity, confidence-weighted alert, sent directly to the response team. The tools didn’t change.

The Payoff: Confidence Without Complexity

This isn’t about building a perfect detection engine. It’s about reducing noise and improving signal fidelity.

By moving from isolated monitoring to behavioural correlation, the IT team reduced their alert volume by 70%, while improving detection coverage across common attack paths. Analysts had fewer, but more meaningful, alerts to review. And when a genuine compromise occurred two months later, this time via a contractor email compromise, they contained it in under 30 minutes.

Not because a tool shouted. But because the system was finally listening for the right things. Is your alert volume a strength, or your biggest blind spot?