Skip to content
Advertisements

Posts from the ‘Forrester Zero Trust Framework’ Category

Machine Learning Is Helping To Stop Security Breaches With Threat Analytics

Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users’ behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up.

Separating Security Hacks Fact from Fiction

It’s time to demystify the scale and severity of breaches happening globally today. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren’t expending the time and effort to hack into systems; they’re looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon’s 2019 Data Breach Investigations Report, ‘Phishing’ (as a pre-cursor to credential misuse), ‘Stolen Credentials’, and ‘Privilege Abuse’ account for the majority of threat actions in breaches (see page 9 of the report).

It only really takes one compromised credential to potentially impact millions — whether it’s millions of individuals or millions of dollars. Undeniably, identities and the trust we place in them are being used against us. They have become the Achilles heel of our cybersecurity practices. According to a recent study by Centrify among 1,000 IT decision makers, 74% of respondents whose organizations have been breached acknowledged that it involved access to a privileged account. This number closely aligns with Forrester Research’s estimate “that at least 80% of data breaches . . . [involved] compromised privileged credentials, such as passwords, tokens, keys, and certificates.”

While the threat actors might vary according to Verizon’s 2019 Data Breach Investigations Report, the cyber adversaries’ tactics, techniques, and procedures are the same across the board. Verizon found that the fastest growing source of threats are from internal actors, as the graphic from the study illustrates below:


Internal actors are the fastest growing source of breaches because they’re able to obtain privileged access credentials with minimal effort, often obtaining them through legitimate access requests to internal systems or harvesting their co-workers’ credentials by going through the sticky notes in their cubicles. Privileged credential abuse is a challenge to detect as legacy approaches to cybersecurity trust the identity of the person using the privileged credentials. In effect, the hacker is camouflaged by the trust assigned to the privileged credentials they have and can roam internal systems undetected, exfiltrating sensitive data in the process.

The reality is that many breaches can be prevented by some of the most basic Privileged Access Management (PAM) tactics and solutions, coupled with a Zero Trust approach. Most organizations are investing the largest chunk of their security budget on protecting their network perimeter rather than focusing on security controls, which can affect positive change to protect against the leading attack vector: privileged access abuse.

The bottom line is that investing in securing perimeters leaves the most popular attack vector of all unprotected, which are privileged credentials. Making PAM a top priority is crucial to protect any business’ most valuable asset; it’s systems, data, and the intelligence they provide. Gartner has listed PAM on its Top 10 Security Projects for the past two years for a good reason.

Part of a cohesive PAM strategy should include machine learning-based threat analytics to provide an extra layer of security that goes beyond a password vault, multi-factor authentication (MFA), or privilege elevation.

How Machine Learning and Threat Analytics Stop Privileged Credential Abuse 

Machine learning algorithms enable threat analytics to immediately detect anomalies and non-normal behavior by tracking login behavioral patterns, geolocation, and time of login, and many more variables to calculate a risk score. Risk scores are calculated in real-time and define if access is approved, if additional authentication is needed, or if the request is blocked entirely.

Machine learning-based threat analytics also provide the following benefits:

  • New insights into privileged user access activity based on real-time data related to unusual recent privilege change, the command runs, target accessed, and privilege elevation.
  • Gain greater understanding and insights into the specific risk nature of specific events, computing a risk score in real time for every event expressed as high, medium, or low level for any anomalous activity.
  •  Isolate, identify, and track which security factors triggered an anomaly alert.
  • Capture, play, and analyze video sessions of anomalous events within the same dashboard used for tracking overall security activity.
  • Create customizable alerts that provide context-relevant visibility and session recording and can also deliver notifications of anomalies, all leading to quicker, more informed investigative action.

What to Look for In Threat Analytics 
Threat analytics providers are capitalizing on machine learning to improve the predictive accuracy and usability of their applications continually. What’s most important is for any threat analytics application or solution you’re considering to provide context-aware access decisions in real time. The best threat analytics applications on the market today are using machine learning as the foundation of their threat analytics engine. These machine learning-based engines are very effective at profiling the normal behavior pattern for any user on any login attempt, or any privileged activity including commands, identifying anomalies in real time to enable risk-based access control. High-risk events are immediately flagged, alerted, notified, and elevated to IT’s attention, speeding analysis, and greatly minimizing the effort required to assess risk across today’s hybrid IT environments.

The following is the minimum set of features to look for in any privilege threat analytics solution:

  • Immediate visibility with a flexible, holistic view of access activity across an enterprise-wide IT network and extended partner ecosystem. Look for threat analytics applications that provide dashboards and interactive widgets to better understand the context of IT risk and access patterns across your IT infrastructure. Threat analytics applications that give you the flexibility of tailoring security policies to every user’s behavior and automatically flagging risky actions or access attempts, so that you’ll gain immediate visibility into account risk, eliminating the overhead of sifting through millions of log files and massive amounts of historical data.
  • They have intuitively designed and customizable threat monitoring and investigation screens, workflows, and modules. Machine learning is enabling threat analytics applications to deliver more contextually-relevant and data-rich insights than has ever been possible in the past. Look for threat analytics vendors who offer intuitively designed and customizable threat monitoring features that provide insights into anomalous activity with a detailed timeline view. The best threat analytics vendors can identify the specific factors contributing to an anomaly for a comprehensive understanding of a potential threat, all from a single console. Security teams can then view system access, anomaly detection in high resolutions with analytics tools such as dashboards, explorer views, and investigation tools.
  • Must provide support for easy integration to Security Information and Event Management (SIEM) tools. Privileged access data is captured and stored to enable querying by log management and SIEM reporting tools. Make sure any threat analytics application you’re considering has installed, and working integrations with SIEM tools and platforms such as Micro Focus® ArcSight™, IBM® QRadar™, and Splunk® to identify risks or suspicious activity quickly.
  • Must Support Alert Notification by Integration with Webhook-Enabled Endpoints. Businesses getting the most value out of their threat analytics applications are integrating with Slack or existing onboard incident response systems such as PagerDuty to enable real-time alert delivery, eliminating the need for multiple alert touch points and improving time to respond. When an alert event occurs, the threat analytics engine allows the user to send alerts into third-party applications via Webhook. This capability enables the user to respond to a threat alert and contain the impact of a breach attempt.

Conclusion 
CentrifyForresterGartner, and Verizon each have used different methodologies and reached the same conclusion from their research: privileged access abuse is the most commonly used tactic for hackers to exfiltrate sensitive data. Breaches based on privileged credential abuse are extremely difficult to stop, as these credentials often have the greatest levels of trust and access rights associated with them. Leveraging threat analytics applications using machine learning that is adept at finding anomalies in behavioral data and thwarting a breach by denying access is proving very effective against privileged credential abuse.

Companies, including Centrify, use risk scoring combined with adaptive MFA to empower a least-privilege access approach based on Zero Trust. This Zero Trust Privilege approach verifies who or what is requesting privileged access, the context behind the request, and the risk of the access environment to enforce least privilege. These are the foundations of Zero Trust Privilege and are reflected in how threat analytics apps are being created and improved today.

Advertisements

Analytics Are Empowering Next-Gen Access And Zero Trust Security

Employee identities are the new security perimeter of any business.

80% of IT security breaches involve privileged credential access according to a Forrester study. According to the Verizon Mobile Security Index 2018 Report, 89% of organizations are relying on just a single security strategy to keep their mobile networks safe. And with Gartner predicting worldwide security spending reaching $96B this year, up 8% from 2017, it’s evident enterprises must adopt a more vigilant, focused strategy for protecting every threat surface and access point of their companies. IT security strategies based on trusted and untrusted domains are being rendered insufficient as hackers camouflage their attacks through compromised, privileged credentials. It’s happening so often that eight in ten breaches are now the result of compromised employee identities.

Thus, taking a Zero Trust Security (ZTS) approach to ensure every potential threat surface and endpoint, both within and outside a company, is protected, has become vital in today’s dynamic threat landscape. ZTS is an essential strategy for any digital business whose perimeters flex in response to customer demand, are using the Internet of Things (IoT) sensors to streamline supply chain and production logistics, and have suppliers, sales teams, support, and services all using mobile apps.  ZTS begins with Next-Gen Access (NGA) by providing companies with the agility they need to secure applications, devices, endpoints, and infrastructure as quickly as needed to support company growth. Both NGA and ZTS are empowered by analytics to anticipate and thwart a wide variety of cyber threats, the most common of which is compromised credential access.

How NGA Leverages Analytics to Secure Every Endpoint

NGA validates every access attempt by capturing and quickly analyzing a wide breadth of data including user identity, device, device operating system, location, time, resource request, and several other factors. As NGA is designed to verify every user and access attempt, it’s foundational to attaining Zero Trust Security across an IT infrastructure. One of the fascinating areas of innovation in enterprise security today is the rapid adoption of analytics and machine learning for verifying users across diverse enterprise networks. NGA platforms calculate and assign a risk score to every access attempt, determining immediately if verified users will get immediate access to resources requested, or be asked to verify their identity further through Multi-Factor Authentication (MFA).

Machine learning-based NGA platforms including Centrify calculate a risk score that quantifies the relative level of trust based on every access attempt across an IT infrastructure. NGA platforms rely on machine learning algorithms to continuously learn and generate contextual intelligence that is used to streamline verified user’s access while thwarting many potential threats ― the most common of which is compromised credentials. IT security teams can combine the insights gained from machine learning, user profiles, and contextual intelligence to fine-tune the variables and attributes that calculate risk scores using cloud-enabled analytics services.  An example of Centrify’s Analytics Services dashboard is shown below:

Visibility and Analytics are a Core Pillar of ZTS

Analytics, machine learning and their combined potential to produce contextual intelligence, real-time risk scores, and secure company perimeters to the individual access attempt level need a continual stream of data to increase their accuracy. Forrester’s Zero Trust Framework, shown below, illustrates how an enterprise-wide ZTS security strategy encompasses workloads, networks, devices, and people.  NGA is the catalyst that makes ZTS scale into each of these areas. It’s evident from the diagram how essential visibility and analytics are to a successful ZTS strategy. NGA provides incident data including reports of anomalous or atypical login and attempted resource behavior. Visibility and analytics applications from IBM, Splunk, Sumologic, and others are relied on to aggregate the data, anticipating and predicting breaches and advanced attacks. The result is a ZTS security strategy that begins with NGA that flexes and scales to the individual perimeter level as a digital business grows.

Source: What ZTX Means For Vendors And Users, Forrester Research Blog, January 23, 2018., Chase Cunningham, Principal Analyst.

Conclusion

Every company, whether they realize it or not, is in a race against time to secure every threat surface that could be compromised and used to steal or destroy data and systems.  Relying on yesterday’s security technologies to protect against tomorrow’s sophisticated, well-orchestrated threats isn’t scaling. Reading through the Verizon Mobile Security Index 2018 Report illustrates why Zero Trust Security is the future. Improving visibility throughout the network and reducing the time to breach detection, stopping malware propagation and reducing the scope and cost of internal and regulatory-mandated compliance requirements are just a few of the business benefits. Analytics and machine learning are the fuel enabling NGA to scale and support ZTS strategies’ success today.

%d bloggers like this: