Examining the Role of Machine Learning and Analytics in Proactive Cybersecurity
As remote and hybrid work become the standard model for business operations, cyber-attacks continue to grow in volume and complexity.
The year 2021 was riddled with news of high-profile breaches such as the Colonial Pipeline breach, the JBS Ransomware Attack, and the Log4J Shell Exploit Activity, among others.
Ransomware emerged with a vengeance impacting any enterprises. An IBM study conducted in mid-2021 reported the escalating cost of data breaches. According to the survey, data breaches, on average, cost those surveyed $4.24 million per incident ‚ÄĒ the highest in the 17 years of this report being produced.
Drastic operational shifts, especially the inclination towards remote work, remain the key drivers of upending the cybersecurity landscape. As software connects the world, it also poses a threat to the security perimeter that previously protected the enterprise.
Proactive Cybersecurity - The Need of the Hour
The need for the interconnectedness of the enterprise for collaboration, innovation, agility, resilience, and business continuity compels enterprises to augment their digital capabilities through technology adoption.
However, this move also demands security controls / countermeasures to evolve and move at the same pace as digital adoption. Studies suggest that security may have become an afterthought and lagged.
The threat landscape is evolving at breakneck speed, rendering the enterprise more vulnerable than ever. Today, a whole system can be breached with a single vulnerability and cause massive damage to the enterprise.
Here's a rundown of the quantifications provided by different studies to better elucidate the scenario:
- Cybercriminals can penetrate 93% of company networks
- Corporate cyber-attacks increased by 50% in 2021
- 43% of cyber-attacks are aimed at small businesses, while only 14% are positioned to defend themselves
- 69% of businesses believe that cyberattacks are becoming more targeted
- 40% of small businesses face a severe cyberattack that results in a minimum downtime of 8 hours
- 85% of MSEs consider ransomware as one of the biggest threats to their SMB customers
- Only 57% of businesses hit by ransomware recovered their data by using a backup
With a growing number of soft targets, and cybersecurity threats like ransomware going on a rampage, taking a proactive stand toward security is non-negotiable.
The Role of ML and Analytics in Cybersecurity
The spiraling complexity of cyberattacks makes it imperative to move beyond the traditional reactive approach toward security and replace it with a proactive PDR (Prevent ‚Äď Detect ‚Äď Respond) strategy.
Taking an advanced approach to cybersecurity demands a data-backed strategy powered by analytics and technologies like Machine Learning (ML).
ML-powered analytics create security systems that can outsmart attacking methods and techniques. This becomes incredibly impactful for those environments that involve vast volumes of data.
To improve their cybersecurity posture, enterprises today have to continuously track and correlate millions of external and internal data points across their infrastructure and users. Manually managing this activity is not only effort-intensive but also error-prone due to the increased cognitive load.
Leveraging technologies such as ML to drive automation and analytics allows organizations to adopt a more proactive security stand. Isolating incidents and detecting those that need deep human analysis becomes faster and easier as these technologies can proactively locate anomalies in numbers, patterns, and behaviors.
How ML Helps in Improving Cybersecurity
ML and AI can help organizations improve their cybersecurity stand, giving them the capacity to analyze data, and detect and recognize complex patterns. These technologies can be employed to create security models and algorithms that proactively identify threats and also predict what future attacks could look like.
Owing to the same, ML and AI are being rapidly used to power up Information Security Solutions such as SIEM, DLP, NGFW, NGAV, EDR, Email filtering, and many more.
AI- and ML-powered models can analyze massive volumes of data at lightning-fast speed and capably predict behaviors in a way humans cannot. These models help cybersecurity teams create adequate threat profiles using existing data and help them identify where the next threat could emerge from. These teams can then create security barriers and proactively respond to the threats.
ML algorithms can effectively and continuously monitor network behavior to identify anomalies. ML can analyze previous cyber-attack datasets and determine which areas of networks were mostly involved in particular attacks. The capability to process and analyze massive data volumes allows it to detect threats such as policy violations, malware, or even internal threats.
Identifying bad neighbors also becomes easier with ML. This technology can proactively monitor internet activity, automatically identify attack infrastructures staged for current and emergent threats and prevent people from connecting to malicious websites.
Using ML, it's also easier to identify cybersecurity attacks such as phishing traps. This is important since the security perimeter is blurring owing to workplace policies such as hybrid and remote work.
Reports show that compromised user credentials, at 20%, were the single most common method of gaining entry. Over 80% of the employees admit to reusing passwords unsafely. Since changing user behavior cannot pass as a security strategy, organizations must use technologies such as ML that identify any virus or malware by its abnormal behavior rather than its signature.
Stressing the Proactivity of ML
Ramping up endpoint security has never been more critical than today. ML has also become a vital technology to detect malware that could potentially be running on endpoints. ML can help organizations ensure endpoints are suitably protected from malware by assisting organizations to proactively identify new malicious files and activities based on known malware characteristics and behavior models.
Locating and analyzing suspicious activity in the cloud also becomes proactive with ML at work. Enterprises can use ML to successfully identify suspicious cloud app login activity, detect location-based anomalies, and conduct IP reputation analysis with velocity and ease.
The technology makes it easier and faster to detect malware in encrypted traffic. The algorithms assist enterprises in identifying encrypted traffic data elements in standard network telemetry while pointing out malicious patterns to locate, isolate and identify threats hidden with encryption.
With the move toward hybrid work, one part of improving cybersecurity rests with improving user behavior. While educating, training, and motivating employees to embrace safe practices continue, CISOs must ramp up their security posture and make it more proactive.
Employing technologies like ML and AI will become imperative to make cybersecurity efforts more competent and enhance the enterprise's capacity to identify, isolate and rectify breaches while keeping the impact footprint small.
At Xoriant, we strive to bring our clients the best in security solutions, ensuring their data-driven infrastructure is protected against the latest threats.
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