AI and Automation Helping Organizations to Combat Cyber Crime

AI, ML, Deep learning, Cybercrime, cybersecurity, artificial neural network AI, ML, CISO
AI and Automation Helping Organizations to Combat Cyber Crime

While the spend on cybersecurity is increasing, organizations must not immediately trust any vendor that claims to use AI-based Security tools

The spending on cybersecurity solutions is increasing at a rapid pace. According to the report on Global Spending on Cybersecurity by IDC, the spending on security in 2019 is predicted to grow by 10% from 2018. Large organizations account for almost two-thirds. Some experts have also predicted that the total cost of cybercrime can exceed $2 trillion by the end of 2019.

Also read: ITS Partners With Radiflow to Add OT Cybersecurity Services for its Process Manufacturing Customers in the UK

The organizations have to anticipate and block 100% of the cyber-attacks that they encounter, while the cybercriminals need to exploit only the unpatched system or a single misconfigured device to get in.

To win the war against cybercriminals, CISOs need to rethink the security strategy, as well as change the security paradigm. Some tips by experts include:

  • Starting with Security: It is recommended that network building must be done with a strong foundation of security, rather than overlaying it on other applications as an afterthought. The security policies must flow seamlessly and enforce policies consistently across the distributed network. This includes the core network to the cloud network and from the OT network to the branch offices.
  • Exploiting the economics of Cybercriminals: Like any organization, cybercriminals are also subjected to the same financial restraints. Most criminals prefer to target using known exploits as developing new tools and the zero-day attacks are expensive. According to experts, a lot of risks can be eliminated by doing regular security exercise that includes discovering and removing security gaps, settling on an integrated security framework based on interoperability, centralizing visibility and control, segmenting the network to restrict the lateral movement of malware and high performance, and deep integration.

Unlike automation and ML, AI tries to replicate the analytical processes of human intelligence and enables decision-making at machine speed. It can deliver predictive security notifications as well. Since this technology is a lot more challenging to use, organizations need to be skeptical as they evaluate any vendor that claims to offer AI-based security.

A true AI system needs and artificial neural network (ANN) that is combined with deep-learning models to accelerate data analysis & decision making. It also enables the network to adapt as well as evolve while encountering new information.

Many cybersecurity companies do claim that they have introduced AI capabilities, but most fall short of true AI because due to the underlying infrastructure that is either too small or has incomplete learning models. Many companies also refuse to reveal the methods, which raises concerns about the reliability of their AI-based solutions.

If done right, AI-based security systems can give organizations an advantage over sophisticated cybercriminals.

Also read: Active.Ai and Visa Partnering to Enable Conversational AI Solutions