# Protecting the Enterprise Network: a People-Centric Approach
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2017-07-21
Category: BUSINESS
Category URL: https://financedigest.com/category/business
Meta Title: How a People-Centric Approach Can Reduce Network Security
Meta Description: Learn from Exabeam's Chief Data Scientist on reducing cyber risks by focusing on user behaviour and machine-learning tools.
URL: https://financedigest.com/protecting-the-enterprise-network-a-people-centric-approachhtml

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**_Derek Lin, Chief Data Scientist at Exabeam explains why adopting a people-centric approach to network security can significantly reduce the risk of breaches_**

The rising tide of cyber threats has placed the issue of network security in the spotlight. These days, protecting against insider and external threats has become a business imperative – one that has seen IT teams employ traffic flow monitoring and threat analysis tools in a bid to beef up network defences.

Despite these investments, [security breaches continue](https://www.financedigest.com/hybrid-working-and-the-continued-security-challenges-for-financial-organisations.html "Hybrid working and the continued security challenges for financial organisations") to take place with alarming regularity. That’s because phishing, whaling, credential compromise and malicious insider threats all have one thing in common: human vulnerabilities. For a phishing attack, the user clicks on a suspicious link while in the case of a malicious insider, an individual chooses to steal company data. Ultimately, the success of these attacks depends on the susceptibility of people.

With hackers now actively targeting users in order to infiltrate the enterprise, IT teams can no longer rely on simply monitoring network traffic alone. What’s needed is a more people-centric approach that monitors user behaviours too.

**Who is accessing the network – and why?**

Today’s mobile and cloud-enabled workers hold multiple IDs giving them [access rights to a plethora of applications and systems; everything from a standard Windows ID to accounts for SAP, Salesforce.com and Oracle](https://www.financedigest.com/meta-denies-kazakh-claim-of-exclusive-access-to-facebooks-content-reporting-system.html "Meta denies Kazakh claim of exclusive access to Facebook’s content reporting system"). Throw BYOD into the mix and tracking ID for each user in one central location becomes nigh on impossible 3Ž4 makings it difficult to know, with certainty, precisely who is accessing the network.

But that’s not all. Without centralised asset monitoring in place, IT teams will have limited visibility of exactly what assets are held on the network. So, while it may be able possible to ascertain who is accessing which server, it will be difficult to know what other sensitive information is being held on that same server.

What’s more, even when IT teams are able to [track who’s accessing what on the network it’s unlikely they could say](https://www.financedigest.com/electric-car-maker-lucid-on-track-for-2022-2023-output-targets-ceo-says.html "Electric car maker Lucid on track for 2022, 2023 output targets, CEO says") for sure if this constitutes a ‘normal’ or ‘suspicious’ behaviour, since network flow monitoring won’t capture the contextual data required to answer such a question.

The good news is that machine-learning technologies can help address all these issues, giving IT teams the visibility they need to improve security practices.

**Utilising user behaviour intelligence**

Machine learning techniques can address the challenge of identity-based threats by providing a complete picture of all user behaviour, whether it’s risky or not, and a complete view of all activity on the enterprise network.

So, when an employee logs in from their office desk using their personal credentials and later in the day logs in remotely via a personal device from home, [machine learning engines](https://www.financedigest.com/machine-learning-engineer-jobs.html "Machine learning engineer jobs") are able to connect these two actions using behavioural data.

But that’s not all. By tracking each employee’s actions over time, machine learning [engines can build up accurate models](https://www.financedigest.com/mercedes-benz-launches-e-class-its-last-new-combustion-engine-model.html "Mercedes-Benz launches E-Class, its last new combustion engine model") of their network activity. Using these baselines, the normal activity for every user can be determined – making it faster and easier to spot new or previously unidentified threat behaviours.

As well as providing the much [needed context required to analyse trends on a per-user basis and spot any activity that deviates from what is considered acceptable](https://www.financedigest.com/why-ev-charging-stations-need-to-accept-open-cashless-payments.html "Why EV Charging Stations Need to Accept Open Cashless Payments ") or ‘normal’, machine learning techniques can also be utilised to build accurate network asset models that give IT teams a true depiction of everything that’s on the network.

Armed with this detailed knowledge IT teams are better able to keep a close eye on what’s being accessed at any particular time, tagging high risk assets – such as those used by [board members](https://www.financedigest.com/passendo-powers-up-with-raft-of-new-board-members.html "Passendo powers up with raft of new board members") or senior managers, for example – to ensure these are subjected to more stringent security measures.

**Initiating effective** [network security](https://www.financedigest.com/new-demands-on-network-security.html "NEW DEMANDS ON NETWORK SECURITY")

Today’s powerful machine learning platforms are now cost-effective enough, from a compute power perspective, for any size of [business to benefit from the real-time output that can be used to significantly improve](https://www.financedigest.com/how-can-call-tracking-improve-your-business.html "How can call tracking improve your business?") security practices.

Delivering increased visibility into the activities of users on their network, IT teams are able to determine who is accessing the network, what they are doing and whether they should be doing it. All of which makes it easier to [detect and respond to modern attacks](https://www.financedigest.com/how-to-detect-and-defend-from-seo-attacks-and-customer-complaints.html "How to Detect and Defend from SEO Attacks and Customer Complaints") fast and in a way that’s not possible using traffic monitoring tools alone.


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