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Identity Based Attacks: Why You Need Behavioral Detection in XDR

  • Srestha Roy

You’ve likely invested in traditional security tools that monitor failed logins or privilege requests—but more advanced threats use legitimate credentials to hide. If attackers bypass authentication protocols or hijack stolen tokens, they can roam freely under the radar. That’s why behavioral detection in an XDR solution is crucial. It does not just look at logs — it looks at patterns. This approach empowers you to detect identity-based attacks like credential stuffing, spectrum-Broken authentication, and privilege escalation early, well before attackers escalate damage.

How Can Behavioral Detection Help You Spot Hidden Credential Threats?

You are missing small cues that indicate credential theft

Many systems flag failed attempts or outright refusals. Meanwhile, credential stuffing can happen in slow, methodical ways—like a user logging in from a nearby IP twice an hour. You might not notice anything unusual—unless behavior is monitored in relationally. Behavioral anomaly detection for identity-based attacks notices when something deviates from a user’s norm. So, if you see rapid logins from odd IP switches or access to systems outside typical hours, that is a red flag—letting you step in before attackers escalate.

Questions to Consider:

  • Are you capturing geolocation and device details on every successful login?
  • Do you correlate small numbers of failed and successful attempts as a single “credential stuffing” incident?

Pro Tip: Tune your XDR to alert on even low volume, distributed login attempts—attackers often stagger their attempts to evade bulk failure alerts. 

Spotting these subtle shifts early means you can lock down compromised accounts before they are used to breach deeper systems.

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Tokens are reused because sessions never expire

You assume session tokens maintain expected behavior—but when applications do not enforce session refreshes, attackers can exploit lingering access. You will not know until the attacker moves across your network. When a session starts performing unusual API calls without proper re-authentication, that signals broken authentication. Behavioral analytics catches these shifts and lets you revoke the rogue token before data gets breached.

Questions to Consider:

  • Have you defined clear maximum session lifetimes for each critical application?
  • Does your platform detect API calls that occur without a valid, recent authentication event?

Pro Tip: Integrate your Session management policies with behavioral analytics so that any session activity outside normal lifespans triggers an automatic token revocation. 

Catching these token reuse attacks in real time stops stealthy intruders from pivoting through your APIs under the guise of a valid session.

Shared credentials mask misuse across teams

If everyone uses the same service account, you cannot trace usage patterns. Say you find that shared account accessing your financial systems at 2 am—that is not normal. Behavioral-based threat detection tracks that you need. Suddenly, you see the anomaly, and you can lock the account or prompt for MFA, safeguarding your infrastructure.

Questions to Consider:

  • Are you tracking usage patterns for each shared credential separately from personal accounts?
  • Can you pinpoint when a shared account suddenly touches systems outside its normal scope?

Pro Tip: Combine behavioral detection with regular credential rotations so that even if a service account is compromised, its window of exploitation is extremely limited. 

By profiling shared credentials, you turn a blind spot into an early warning light—protecting sensitive resources from unauthorized service account abuse.

Privilege escalations fly under the radar

Adding a user to an admin group might slip through without scrutiny—especially if done off-hours or via scripts. Unless someone reviews change logs diligently, these shifts can remain masked. Behavioral analytics watches event timing and context: a non-admin granted domain rights outside normal processes sets off an alert. That gives you the opportunity to reverse the escalation and investigate before it becomes a bigger foothold.

Questions to Consider:

  • Do you log every role and group update with timestamps and source details?
  • Does your XDR correlate privilege changes with subsequent critical actions on that account?

Pro Tip: Automate immediate alerts and rollback procedures for any privilege change originating outside standard workflows or business hours. 

Catching unauthorized privilege escalations in the act prevents attackers from abusing elevated rights to move laterally or exfiltrate data.

What Makes Behavioral Detection Essential in an XDR Platform?

  • It personalizes protection by learning each user’s normal patterns.
    You’ve seen countless alerts for unusual logins, but how do you know which ones really matter to each person?
    An analyst in your finance team might routinely pull large reports at midnight, while a dev in your engineering org never strays into those systems. When the system builds a behavioral profile for each identity—tracking which applications they use, at what times, and from which locations—it can immediately spot when John in Finance suddenly runs queries against production SSH servers. This isn’t just another “failed login” alert; it’s a clear deviation from John’s normal rhythm. By flagging these true anomalies, you cut through the noise and zoom in on genuine threats, so you can stop a compromised account before it spirals.
  • It links signals across identity, network, and endpoints to reveal the full attack path.
    A single alert—say, a user accessing an unusual API—doesn’t tell you much on its own. But what if that API call is immediately followed by a batch of file transfers and a new process of spawning on an endpoint?
    Without stitching those events together, you only see fragments and risk missing the bigger picture. Behavioral detection in XDR ties together identity logs, network flows, and endpoint telemetry into a single, coherent incident. You can literally watch the attacker’s path—from the first credential to steal, through privilege escalation, to data exfiltration—all in one view. That contextual clarity means you do not chase false leads; you go straight to the heart of the breach and shut it down.
  • It prioritizes alerts based on real business impact, not just rules that matches.
    Of course you want to know whenever someone’s credentials are abused—but if every minor hiccup demands your attention, your team will drown in trivial alerts. Behavioral analytics scores each anomaly by combining factors like the identity involved (is this a service account? a director?), the sensitivity of the target (a production database vs. a sandbox), and the attack indicators observed. When an alert involves a critical asset—say, the Active Directory server—during a sudden afterhours login, that event leaps to the top of your queue. Meanwhile, a lowrisk misstep on an intern’s test VM sits patiently until you’re ready for it. This dynamic prioritization empowers you to focus on the threats that put your organization most at risk, rather than getting lost in an avalanche of noise.
  • It enables immediate, automated response to stop attacks in their tracks.
    In a credential-based breach, every second counts. Manually investigating, triaging, and then responding might take your team minutes—or hours—while attackers move laterally, harvest tokens, and deepen their foothold. With behavioral detection baked into XDR, you can define playbooks that trigger the instant an identity anomaly is confirmed. Imagine the system revoking a session token, forcing an MFA challenge, or isolating that user’s devices on the network—all without waiting for human intervention. By automating response based on context rich behavioral signals, you slam the door on attackers at machine speed, turning a potential multiday dwell into a contained incident within seconds.

How Fidelis Elevate Identity-Behavior Capabilities?

From our research across official datasheets and resources, Fidelis Elevate authentically delivers these capabilities:

  • Deep Session Inspection analyzes full network and encrypted traffic, flagging abnormal sessions and dataflows—critical for exposing credential misuse.
  • Active Directory Intercept™ offers integrated AD monitoring, deception, log analysis, and threat mapping.
  • Active Threat Detection correlates with identity, endpoint, and network signals mapped to MITRE ATT&CK—spotting attack sequences.
  • Deception-based detection places traps around identity systems to lure attackers and generate high-confidence alerts.
  • Automated playbooks and workflow actions such as account lock or network isolation can trigger detecting identity anomalies.

You can’t defend identity by counting logins alone—behavioral visibility is critical. Traditional tools miss credential-based and broken authentication attacks because they lack context. But XDR platforms like Fidelis Elevate combine deep session inspection, AD-aware deception, and automated workflows to give you a powerful edge. 

Talk to an expert or request a demo to validate Fidelis Elevates identity-protection capabilities in your environment.

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About Author

Picture of Srestha Roy
Srestha Roy

Srestha is a cybersecurity expert and passionate writer with a keen eye for detail and a knack for simplifying intricate concepts. She crafts engaging content and her ability to bridge the gap between technical expertise and accessible language makes her a valuable asset in the cybersecurity community. Srestha's dedication to staying informed about the latest trends and innovations ensures that her writing is always current and relevant.

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