BTC $3568.51 0.59%
XRP $0.306569 -1.30%
ETH $93.31 -0.75%
show
show
Digital Economy:
What will the global market get

5 Artificial Intelligence and Machine Learning Use-Cases for Cybersecurity

05 December 2018 18:33, UTC   |    6867
5 Artificial Intelligence and Machine Learning Use-Cases for Cybersecurity
By Mark Lynd

Evolving cybersecurity risks and trends continue to force organizations to adopt waves of precautionary changes and solutions to protect themselves, further forcing organizations to adapt, evolve or fall behind in the marketplace. Rapid technological development and adoption across diverse industries presents additional developmental challenges for organizational cybersecurity processes and systems.

Employing Artificial Intelligence (AI) and Machine Learning (ML) can aid in facilitating more secure environments where digital transformation and technologies are being utilized to enhance automation and growth capabilities in compliance with various enterprise models.

#1. Risk Identification with Artificial Intelligence

Risk and mitigation strategies within corporate structures should require rapid inclusion of deep learning (DL), ML, and other AI to reduce human error prevalent in the behaviors individuals possess. To make mistakes is to be human and it presents additional complexity within the cybersecurity infrastructure of any organization.

“Misconfiguration, poor update management, lost devices, avoidable password breeches and other human related incidents result in over 95% of all investigated cybersecurity breaches,” according to IBM’s cybersecurity index. As machines corner abilities to understand technological environments, the innate nature of identifying the risks based on historical data and system breaches strengthens the organizational abilities to circumvent obstructive behaviors, thereby improving their overall security stance.

Discovering potential drivers behind systems susceptible to attacks, configuration errors and potential high-risk threats allow for advanced neutralization and communication protocols to allow leadership to strengthen enterprise-wide cybersecurity details and stance. As risk grows, detection becomes key to understanding how to innovate at a rapid rate while protecting key internal resources and capabilities.  

#2. Early Detection of Cyberspace Incidents

Cyberspace incidents are often provoked utilizing knowledge of pre-specified scenarios and vulnerabilities within existing systems to extrapolate complexities and advance threatening behavior. Application of AI and ML will empower internal capabilities across enterprises to develop and evolve the internal architecture of existing operations platforms to scan for system deficiencies and areas vulnerable to attack. With the ability to record and assess the data from these systems, AI will become more advanced and have the ability to run various predictive scenarios and improve security operations overall.

#3. Operational Improvement of Security Systems

Building resiliency in a growing digital environment complicates the development and advancement of strategic initiatives, given the propensity of underestimating the external abilities of hackers, viruses and weak structural configuration by many enterprises. Reducing the occurrence of incidents and breaches in a technological environment requires strong organizational design, which includes the necessary capabilities and precautions within the infrastructure construction. Where risk is prevalent, extrapolation into the potential positive and negative opportunities that effect cross-functional departments can only be properly assessed by running many different scenarios with the data an organization already has. This is where AI can play a critical role augmenting and empowering the existing security resources.

Implementing ML to actively monitor and review API (Application Programming Interface) calls, external access points and other logs within enterprise systems enables advanced data monitoring and filtering that can be performed around the clock, thereby eliminating some of the human resourcing and security issues. With the ability to aggregate information and internalize data, algorithms can rapidly develop outputs and inputs creating situational models for how data can be used for and against an organization. With this information and utilizing AI after correlating various data sets can result in positive predictive actions against future and current threats, strengthening the security systems within an enterprise.

#4. Organizational Leadership Improving Situational Awareness

Identifying and deploying fundamental security policies and procedures forces organizational responsibility onto the leadership team to develop additional cultural awareness of the severity and risks involved operating in digital environments. Existential threats are inevitable and evolve as organizations evolve; invincibility does not exist in cyber environments. Awareness of the psychological and behavioral intricacies of the individuals operating a digital environment requires intelligent leadership intervention and administrating.

Leadership should strengthen organizational defenses by properly utilizing AI and ML to assess and understand the risk and threats to internal systems that cannot be adequately performed by human capital due to the amount of data or resourcing issues.

Within the McKinsey Quarterly, Thomas SIEBEL notedfailure in digital transformation will result in 40% of existing businesses to fail in the next decade.” Situational awareness within an organization’s cybersecurity practice extends beyond the cyberspace environment into the individuals responsible for the facilitation of operations within the corporate infrastructure. Positive disruption using AI internally can actually promote advancements in a team’s ability to adapt to the evolving requirements and best practices being seen in this era of rapid digital transformation.

#5. Corrective Actions for Cybersecurity Incidents

Security breaches and destruction of technological resources can occur at an alarming rate for organizations that have not properly evolved their security practices to include AI and ML for remediation given the growing complexity of the outside world and their internal environments.

According to Juniper Research, cybercrime is significantly “increasing these costs and will result in over $2 trillion in expenses by next year.” To thwart the potential of an attack and mitigate the damage that may occur, it’s pertinent for organizations to consider the opportunity costs necessary for managing detection and response through the automation of highly manual and labor-intensive activities to preserve and improve data crucial to organizational development, operation and growth. The ability to utilize AI to automate and create advance notification of threats in real-time will reduce expenses and create rectifiable paths when correlating analytics behind the scenes regarding why and how a breach or security incident occurred.

Utilizing AI properly will reduce the appearance of actual and false threats, thereby automating the process of digital remediation and allowing an organization to reach its fuller potential commercially.

Mark Lynd, Ranked in Top 15 for Digital Transformation, Blockchain and Crypto, ranked in Top 15 - Digital Transformation Power 100 and Ranked in Top 50


Read the best crypto news analysis here! bitnewstoday.com Bitcoin, investments, regulation and other cryptocurrencies
Found a mistake? Select the text and press CTRL+ENTER
Leave a comment
Leave a comment
Report a mistake