AI in Cybersecurity

InnMind presents you review of CBInsights report about AI in Cybersecurity: funding history, market breakdown, patents, forward looking trends.

Ai in cybersecurity infogrpahic

Information for this article was taken from CBInsights report “AI in Cybersecurity”

Everybody knows how Artificial Intelligence can transform our lives. Let’s look at AI in Cybersecurity.

AI in cybersecurity is used to monitor activity on systems and networks in real-time, identify patterns from internal and external data-streams, speed up detection, free-up resources, enable faster remediation, and help improve continuous cyber resilience.

AI can really improve cybersecurity and here are some the advantages of AI in Cybersecurity:

➕ AI can automatically detect, investigate, classify, and remediate advanced types (viruses, worms, trojans, spyware, adware, and ransomware, etc) of malware in real time.

➕ Involves overloading a network with illegitimate traffic to make the host inaccessible to legitimate users. Monitoring traffic is tedious, and sifting through the vast amount is beyond human cognition. AI can proactively and automatically sort through good and bad traffic.

➕ Connected devices lack supporting infrastructure for storing robust cybersecurity policies. Machine learning algorithms can monitor network device traffic to model a baseline and flag anomalies when the normal behavior of the IoT ecosystem is compromised.

➕ Humans are the biggest cybersecurity vulnerability. Eg. spear phishing: attacker exploits an employee’s motivations and role within an organization to acquire credentials etc. AI can augment the ability to catch social engineering tactics used by hackers.

So, not surprisingly, new startup ideas in cybersecurity are getting more investments. In the period from 2012 till 2017 there were 217 deals and $2.1 billion invested. Here is the infographic, which shows you funding history of AI in cybersecurity:

Pictures in the infographic were taken from CBInsights report “AI in Cybersecurity”

Some outcomes from the report:

👉 Deals to cyber-AI startups were up 53% in 2015, and remained fairly constant in 2016. At the current run rate, deals are projected to reach 78 by the end of 2017.

👉 Q1’17 has been the busiest quarter in the last 5 years. The top round in Q1’17 was a $70M Series C.

👉 Deal shares have been largely fluctuating over the years. Early-stage (Seed – Series A) deal share dropped to an all-time low in 2016.

Based on the presented information we can do some forecasts:

📈 Instead of occasional device-level patches, machine learning will help in network-level behavior-analytics and real time entity anomaly detection.

📈 Funding to companies like Wiretap are indicative of solutions geared toward eliminating human susceptibility to error from within the enterprise. AI/ML tools will play a significant role in monitoring human behavior in real time.

📈 As defenses get more robust, so will the nature of attacks that use artificial intelligence to evade detection.

If you are interesting in cybersecurity these publications will be also interesting for you:

2016 Cybercrime Report

The Global Overlook of Cybersecurity Industry

Cybersecurity Market Report

The report covers the business of cybersecurity, including market sizing and industry forecasts from consolidated research by IT analyst firms, emerging trends, cybercrime, employment, the federal sector, notable M&A, venture capital and more.

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