Cyber- attackers are influencing
automation technology at launch strikes, whereas most organizations at rest use
physical efforts to collective internal security findings, also putting them in
a specific context with outside threat data. With the use of these conventional
methods, it can take months or weeks to notice intrusions, during which time invaders
can exploit susceptibilities to compromise systems and take out information. To
deal with these challenges, ongoing organizations are exploring the use of
artificial intelligence (AI) in their day-to-day cyber risk management
operations.
According to a technology market report, more than 70% of attacks exploit known
vulnerabilities with available patches. Similarly, the findings show that
hackers take benefit of vulnerabilities within minutes of their becoming public
information. Such statistics emphasize the importance of time-to-remediation. But,
because of lack of security professionals and the general challenge of dealing
with big data sets in safety, it is not astonishing that vulnerability
remediation efforts are not keeping up with cyber challengers. Current industry
research proves that it takes organizations on average 146 days to fix critical
vulnerabilities. Noticeably, this
benchmark points out you need to rethink existing approach to enterprise
security.
Cyber challengers have long influencing
machines and automation systems to streamline their operations. Therefore why
shouldn’t organizations do the same?
Identification
of threats:
Organizations face a rising battle
when it comes to cyber security, as the attack surface they have to protect has
extended importantly and is predicted to balloon even further. In previous
times, it was adequate to focus on endpoint protection and network, however now
with applications, cloud services, and mobile devices (e.g., mobile phones,
tablets, Bluetooth devices, and smart watches) Organizations are battling a
largely completed attack surface.
This ‘deeper and wider’ attack surface
just attaches to the existing problem of how to manage the velocity, volume and
complexity of information generated by the myriad of IT and security tools in a
firm. The feeds from these disconnected techniques should be analyzed,
remediation, and normalized effort prioritized. The more difficult the challenge, the more
tools, and the broader the attack surface, the more data to analyze. Conventionally,
this approach required legions to staff to comb during the huge amount of data
to connect find latent dangers and the dots. Such efforts took months, for the
period of which time attackers utilized vulnerabilities and took out information.
Breaking down existing automating conventional
security operations tasks and silos, thus, technology has helped to become a
force-multiplier for augmenting scarce cyber
security operations talent. In this context, the use of human-interactive
machine learning engines can mechanize the aggregation of data across different
data types; data of map assessment to compliance requirements; and normalize
the information to rule out false- positive, enrich data attributes and duplicates.
Risk
Assessment:
Once internal security intelligence is
contextualized with external threat information (e.g. malware, exploits, threat
actors, reputational intelligence), such finding should be inter-related with
business criticality to identify the actual risk of the security gaps and their
ultimate impact on the organization.
Eventually, unknowing the impact a
‘coffee server’ has on the business assessed to an ‘email server’, makes it nearly
impossible to aim at remediation efforts on what actually matters. In this
context, human- interactive machine learning and advanced algorithms play a big
role in driving the exact response to individual risks.
Orchestration
of Remediation:
Increasing teamwork between security
teams that are responsible for recognizing
security gaps and IT operations teams which are focused on remediating them, carry
on to be challenge for many firms. Through setting up thresholds and
pre-defined policies, groups can also plan remediation actions to fix security gaps
in a well-timed trend.
Procuring machine learning to do the
heavy lifting in first line security information assessment facilitates analysts
to aim at more progressed researches of threats rather than performing strategic
information crunching. This meeting of the minds, by which, Artificial Intelligence is applied
using a human-interactive approach grasps lots of promise for responding, fighting,
and detecting to cyber risks.
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