Who do you trust? Your doctor? Perhaps your golf caddy? Maybe your Uber driver? Trust can be difficult to earn. And once broken, trust can seem impossible to restore.
For a case in point, look no further than the cybersecurity industry. According to Risk Based Security, data breaches in 2019 increased 33% over the previous year. It culminated in 5,183 breach instances and a staggering 7.9 billion exposed records.
Of course, there are numerous different causes behind such a sizable amount of security incidents. But for many organizations, it was the actions taken by those given trust – employees, contractors, partners and others – that rendered them data breach victims. For others it was trusted connections with devices or Internet facing systems that proved to be the flaw.
Given that, who can you trust from a cybersecurity perspective? Can you trust your IT admins with privileged access to not snoop into sensitive files? Do you trust your account managers to not send customer information to their personal accounts for potential use in another job? Can you trust the disgruntled employee who received a poor performance review to not steal and expose company data out of a sense of revenge?
Compounding the problem, it’s not just these deliberate acts of malice that cause data breaches. Accidental incidents perpetrated by loyal employees also cause data breaches. It could be the support rep who clicks a link on a phishing email and introduces malware on the network.
Due to the worsening state of cyberattacks and insider threats, it’s imperative for organizations to make the almost paranoid assumption that they can’t trust anyone or anything either inside or outside their network. This is the essence of zero trust, the topic of our recent webinar Security Analytics Make Zero Trust Possible.
Security Analytics Makes Zero Trust Security Model Possible
The zero trust security model implies that all devices, resources, systems, data, users and applications are not to be trusted. And no amount of perimeter defenses, SIEMs or employee training can secure all those vectors. Instead, solutions should be proactive, rather than reactive.
In a zero trust environment, it’s pivotal that organizations can automatically monitor the entire IT environment for signs of malicious activity before an incident occurs. To accomplish that, machine learning based security analytics must play a crucial role.
Security analytics aggregates data from a host of solutions like SIEM, IAM, IGA, PAM, DLP, IDS, CRM, and more. Based on the behaviors of users and entities gleaned from that data it’s then possible to generate dynamic risk scores for cyber threats as they occur. These scores trigger automated risk-response workflows, or alert human personnel to investigate, allowing organizations to neutralize legitimate threats quickly.
In an environment where data breaches now make daily headlines, being able to spot high-risk, abnormal behaviors – regardless of where they originate – is invaluable.
Learn more by watching our on-demand webinar.