WootCloud Blog

AI/ML Series: The Perfect Storm for Cybersecurity

This is the final summary in our 5-part series on AI/ML for cybersecurity summarizing our first 4 posts covering, “How AI and Machine Learning helps customers power True Zero Trust Device security at scale“, part 2, “The Current State of AI/ML in Cybersecurity, part 3, “The Challenges of AI/ML in Cybersecurity, or part 4, “The Opportunities for Using AI/ML in Security“.

Recent Cyberattack Map (Courtesy DigitalAttackMap.com)

The present confluence of IoT surge, accelerating 5G adoption, rising cyberattacks, and shift to work/study-from-anywhere presents a critical need for intelligent, device and infrastructure security.

The urgency is so great, in fact, that the US has prompted a 30-country meeting and agreement to combat ransomware. The White House facilitated meetings on Wednesday and Thursday that brought together representatives from countries on every continent. These countries released a joint statement on Thursday calling ransomware “an escalating global security threat with serious economic and security consequences.”

At the start of the AI/ML for cybersecurity series, we discussed how traditional security firewalls and endpoints were no longer sufficient to protect against AI-powered threat actors.

We discussed how the confluence of the explosion in connected smart devices, the bad guys adopting AI/ML for nefarious purposes, and WFH is leaving organizations large and small wide open to both insider and outside attacks.

And concluding that only with AI/ML does the ability to scale access control reauthorizing every app and API at every request to interact with critical systems by known devices and known users that are in line with their past known behavior is True Zero Trust. 

We tied our product vision in to Zero Trust with WootCloud TrueID™ – our anomaly and threat detection capabilities to continually validate that a device is what it claims to be.

At the start of the series we also offered resources to assess your organization to help you gauge how widespread the use of more advanced implementations of AI/ML is in your peer organizations.

Here is a simple maturity self assessment built from our customer and market interactions:

  • Many of my organization’s important IT systems have evolved over time to be a sprawling aggregation of multiple systems, thus becoming difficult to manage, and likely less secure. 
  • My organization has different people of different skill levels from different teams maintaining applications within this aggregation, often at more basic levels than ideal (ie to pass compliance) leaving areas under-maintained and likely less secure. 
  • For WootCloud customers, the cybersecurity teams are largely resource (read labor) constrained, and need advanced automation, ideally using AI/ML capabilities. My organization falls into this category.
  • AI/ML is widely used in my organization for cybersecurity in basic forms, such as spam and malware detection. 
  • We know malicious actors have attacked our organization’s less secure systems and likely used AI/ML given the speed, sophistication, duration and more. 

This simple checklist can be used to calibrate against past internal surveys and measures. To get deeper insight, use our Device Security Maturity Model.

AI/ML Cybersecurity in Use Today

We illustrated in part 2 of our AI/ML series how intelligent, behavior-based anomaly detection can reveal network threats and attack attempts pre infiltration, during persistence, and when exfiltration attempts are made. 

Infiltration – Intrusions and backdoor attempts usually create anomalous behavior against baselines – whether by unknown users and devices – and/or known users and devices at unusual times, geos, behavior. 

Persistence – Once  attackers are inside the network, they can blend in by using legitimate credentials and applications. If organizations are just looking for known exploits, they won’t be able to find these attackers lurking in their network. Moreover, many organizations focus their efforts on preventing malware and intrusions at the network perimeter, and may not monitor for behavioral anomalies indicative of internal reconnaissance, lateral movement, or data exfiltration.

Exfiltration – Similarly exfiltration post intrusion must be understood and addressed. Your systems need to detect large data transfers from (multiple) devices sent to outside/unknown entities and instruct your network infrastructure to drop traffic to those destinations.

It quickly becomes untenable to protect an organization with manual security policies at this level. Similarly, having AI/ML training to trigger and enforce network access controls in broad strokes, say against an entire team or department in response to an incident, would be similarly ineffective, not to mention less than user friendly. Modern, intelligent tools need to be able to see all devices and activity, baseline and profile behavior to identify anomalies indicative of attack or compromise, then remediate those threats at an individual device level at the moments of compromise as we advertise.

We touched on the challenges in AI/ML for cybersecurity around the building of trust to get signoff to even start with an AI/ML for cybersecurity program, the ever-present skills shortages in both cyber and data science, the modeling and training of AI/ML programs, and more.

Consider a trusted and proven partner in your particular field of AI/ML program deployment if you don’t have a tech-centric organization and/or have a division within that can truly invest the time and resource needed to succeed.

AI/ML-powered Remediation is the Greatest Opportunity

While visibility, inspection, and analysis is great, all of this cannot work at any significant scale without automated enforcement. 

Automatically identifying and eliminating rogue devices to protect your risk posture in moments of compromise while remaining user-friendly is critical in the era of demanding user bases, increasing compliance regulations, and IoT/5G/Work-from-anywhere megatrends.

Maintaining operational hygiene and intelligent asset management is also critical to modern IT in the age of BYOD and remote procurement.

Our agentless, AI/ML-driven platform identifies, analyzes, and manages device and infrastructure assets automatically to help you close security gaps like those – all in real time – all critical with today’s IoT, 5G, and work-from-anywhere megatrends in full swing.

Business Value

Our mission, like yours, is to provide your employees, students, faculty and more with a safe, user-friendly and secure environment by:

  • Giving Full visibility – with RF and Network sensors seeing 100% of your devices and infrastructure encompassing all stages of attack – infiltration, persistence, and exfiltration
  • Reducing Mean Time to Resolution (MTTR )– with AI/ML-powered, automated remediation that drives >70% reduction in threat hunting times
  • Boosting User Experience – by reducing support ticket volumes, lowering alert noise for a >60% gain in operational efficiency (~1.4 FTE Service Savings per site/ per shift)

Each of these benefits on their own can offer 5-6 figure USD savings per year, shortening the payback period on your investment.

The perfect cybersecurity storm has arrived. Consider intelligent device and infrastructure security today.

To learn more in a zero touch, no obligation Demo or POC, please contact us.

Share this post with your network.

Share on linkedin
Share on twitter
Share on facebook
Contributing Authors:

Andreas Stenzel

Share this post with your network.

Share on linkedin
Share on twitter
Share on facebook

This website uses cookies to ensure you get the best experience on our website.