This article originally appeared in Technology Org.
Security strategies often focus heavily on preventing external intrusion, yet the most critical data is accessed daily by thousands of legitimate users. As organizations decentralize into IaaS and SaaS ecosystems, the distinction between “outsider” and “insider” blurs. The threat is often about who is already logged in.
Insider risk is a real and present danger, but not always for the reasons portrayed in movies. While corporate espionage exists, the vast majority of insider incidents stem from negligence, sloppy access habits, or compromised credentials that mask external attackers as employees. Traditional monitoring tools, such as legacy SIEMs and first-generation UEBA, struggle to keep up. They rely on static rules that generate tidal waves of false positives, burying security teams in noise while real risks slip through. According to IBM research, 83% of organizations reported insider attacks in 2024, underscoring just how pervasive insider-driven incidents have become across industries.
To protect intellectual property and customer data today, security teams must pivot toward a context-aware AI SOC approach. Here are six key strategies for rethinking insider risk.
1. Redefine the “Insider” (It’s Not Just Malicious Employees)
When building a risk program, many organizations make the mistake of narrowing their focus to the “disgruntled employee.” While the malicious insider is a valid threat vector, they represent the minority of incidents.
An improved definition of insider risk must include three distinct categories:
- The Careless User: The well-meaning employee who misconfigures a cloud storage bucket, shares a sensitive document with “Anyone with the link,” or bypasses security protocols to “get the job done.”
- The Compromised Account: An external attacker who has hijacked the credentials of a legitimate user. To a traditional rule-based system, this looks like an employee; to behavior-based insider risk detection, the subtle anomalies in how they access data reveal the truth.
- The Third Party: Contractors, vendors, and ecosystem partners who require access to your environment but lie outside your direct HR control.
If your detection engineering only looks for malice, you will miss the negligence and credential theft that actually cause the most data breaches.
2. Context is King: Why Static Thresholds Fail
“If a user clones 20 repositories in a day, alert the SOC.”
This type of static, threshold-based logic is the primary driver of alert fatigue. For a new engineering hire, cloning 20 repos is their second day on the job. For a marketing manager who rarely, if at all, touches code, it is a critical anomaly.
Effective security analytics must therefore prioritize context over simple counting by building dynamic baselines at the individual, peer group, and organizational levels. By understanding what is normal for a specific user, their team, and the company as a whole, a system can automatically suppress predictable, benign spikes in activity and reduce alert fatigue in insider threat programs.
3. Follow the Data Signals (Code, SaaS, and DLP)
Insider risk is rarely visible at the endpoint alone. The most high-fidelity signals often live deep within your SaaS applications and code repositories. Security teams need deep visibility into specific actions that indicate data exfiltration or sabotage, rather than just monitoring for USB usage or malware. For instance, a critical signal might be a sudden cloning of multiple repositories by a developer who has never touched those code bases before, or the mass downloading of sensitive artifacts, such as financial workbooks, design docs, or customer lists, from productivity suites like Google Workspace or Microsoft 365.
To increase fidelity, good detection leverages signals from existing solutions, such as Google’s native GCP DLP tags, to add weight to an alert. However, the challenge lies in distinguishing between a legitimate “burst” of work, like a developer pulling code for a new sprint, and a true anomaly. This requires taking in all signals, but strategically assessing them to find the real detections that go beyond simple thresholds. By correlating the content of the data with the context of the user’s role, security teams can accurately identify when a deviation from a historical pattern represents actual risk rather than just a busy workday.
4. Build a Cross-Functional Program (HR + Security)
Insider risk is also a human problem, not just a technical one. Therefore, it cannot be “owned” by the security team in isolation. The most mature insider risk programs actively integrate Legal, HR, and Finance into the workflow to create a holistic view of user behavior. This collaboration allows for business-context-aware rules, where data points such as an employee submitting their resignation or being placed on a Performance Improvement Plan (PIP) can subtly and automatically adjust the risk scoring for that user’s account.
For example, consider a sales director downloading a full client list. In a vacuum, this is standard behavior required for their role. However, if that same sales director submitted their resignation yesterday, the context shifts entirely, turning a routine action into a high-priority data exfiltration alert. Modern tooling must be capable of ingesting these non-technical signals to sharpen detection accuracy, ensuring that security teams are alerted to actual risks without being flooded by false positives or requiring manual intervention to correlate HR events with security logs.
5. Watch for Dormant Accounts and “Fake Hires”
One of the most overlooked aspects of insider risk is the “ghost” account. As companies grow, they invariably accumulate technical debt in the form of unused permissions and forgotten user credentials. Real-world attacks frequently leverage these vulnerabilities, particularly through dormant accounts, stale profiles of former employees or contractors that were never fully offboarded. Because these accounts belong to “valid” users in the system’s history, they become prime targets for attackers looking to move laterally across the network without triggering standard intrusion alarms.
Another subtle but dangerous vector involves “fake hires” or illegitimate test accounts. These are situations where accounts are created for employees who do not exist, or temporary access is provisioned for testing purposes and never revoked, leaving a silent backdoor into the environment. A robust analytics platform should proactively identify these unused entitlements and dormant accounts to limit the blast radius. By surfacing unused permissions for admin accounts and supporting secure offboarding workflows, organizations can drastically reduce their attack surface before an incident ever occurs.
6. Immediate Actions: What to Do Now
If you are looking to harden your posture against insider risk immediately, start with these four steps:
- Audit Privileged Access: Immediately review who has visibility into your most sensitive IP (source code, financial data). If they don’t strictly need it for their current role, as indicated by no use of the permission, revoke it.
- Scrutinize Outbound Flows: Don’t just look at what comes in (phishing); look at what goes out. Small anomalies in outbound data flows often expose the biggest betrayals.
- Check Coverage: Ensure you have visibility across key environments, not just endpoints, but SaaS, email, and code repositories. Blind spots are where insiders operate.
- Leverage Agentic AI: Assume adversaries know how legacy rules work and will try to evade them. Use AI detection that looks for the intent and semantics of behavior, rather than just matching known attack signatures.
A Multi-Model Approach
The era of relying solely on UEBA or generic Large Language Models (LLMs) is ending. UEBA is too noisy; generic LLMs often hallucinate or lack deep security context.
The future of insider risk management lies in a multi-model AI approach. This combines semantic analysis (understanding what the data is and how it’s connected), behavioral analytics (understanding who is touching it and whether they are behaving normally), and specialized LLMs (to synthesize the narrative and make recommendations). By layering these technologies, security leaders can finally filter out the noise of the careless and focus their energy on stopping the compromise.
