Artificial Intelligence + Automation — future of cybersecurity.


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Artificial Intelligence and automation have a bright future in cybersecurity. Using artificial intelligence is a smarter way to detect and manage cyber security threats. Here is my take on it.


Artificial Intelligence and Automation should be used in cyber threat detection to increase security, efficiency and help organizations be pro-active, helping them see the threats in advance and keep their infrastructure and data safe.

As organizations delve into smarter and innovative products, they are dependent on critical data which is under constant threat. A breach of critical data can put the organization and its customers at serious risk. A combination of AI and Automation can be leveraged to counter these threats and provide insight into obscure and malicious activity on systems, networks, and infrastructure.

Artificial Intelligence + Automation — future of cybersecurity helps us to understand the threat landscape.

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Threat landscape

In 2017, the average number of breached records by country was 24,089. The nation with the most breaches annually was India with over 33k files; the US had 28.5k. (Ponemon Institute’s 2017 Cost of Data Breach Study)

Attacks involving cryptojacking increased by 8,500 percent in 2017. (Symantec)

Untold story of NOTPETYA, the most devastating cyberattack in history. (WIRED)

Despite the awareness about these threats, there is very little investment and readiness among businesses to take preventive action and get expert advise till they are hit hence, Artificial Intelligence + Automation is the future of cybersecurity.

Almost every business or organization is digital now and deals with critical data and infrastructure for their operations. Cyber threats not only put a serious dent financially, and time wise to their operations, but also, in terms of customer acquisition and trust.

The Equifax breach cost the company over $4 billion in total. (Time Magazine)

The average cost in time of a malware attack is 50 days. (Accenture)

Including turnover of customers, increased customer acquisition activities, reputation losses and diminished goodwill the cost of lost business globally was highest for U.S. companies at $4.13 million per company. (Ponemon Institute’s 2017 Cost of Data Breach Study)

The average cost per lost or stolen records per individual is $141 — but that cost varies per country. Breaches are most expensive in the United States ($225) and Canada ($190). (Ponemon Institute’s 2017 Cost of Data Breach Study)

Why (AI + Automation) is the solution?

In my opinion, AI + Automation is a great solution for the following reasons.

Cyber security = Security automation + AI

Security automation = Threat monitoring + detection + response

AI = Accuracy

Security Automation:

Smarter threat monitoring
Smarter threat detection
Smarter threat response

What is Security Automation?

Security automation is the automatic handling of a task in a machine-based security application that would otherwise be done manually by a cybersecurity or a webOps engineer.

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What is orchestration?

Security orchestration is the integration of various security applications and processes together.

What is Security Automation and Orchestration?

Security automation and orchestration is coordination of automated security tasks across connected security applications and processes.

It is designed to reduce risks, operational errors, improve efficiency, and to address the Cyber Security threats that often come from erroneous use of data. Manual effort increases time and effort required for the analysis which affects incident response time which is critical in mitigating the threat.

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When using manual security tasks, sifting through a large volume of data is error-prone and also time-consuming. Ideally, any security task should follow the formula —


ST = Security Task
Q = Quick
R = Reliable
A = Accurate

Automated security tasks can handle the quick and reliable part, but, fail with accuracy. Accuracy can be improved using AI, with smart testing data and real-time pattern mapping of cyber threats.

Security automation helps devOps / WebOps teams to be proactive rather than reactive. It also helps the team identify obscure attacks and be prepared for them.

How do we deploy security automation

There are many ways to deploy this to one’s own organization and tailor it to the requirements.

Here are high-level steps of the security automation deployment.

  • Deployment automation
  • Infrastructure automation
  • Security monitoring tools automation
  • Automated threat detection
  • Threat response automation
  • Security workflow automation

Deployment Automation:

Deployment automation allows applications to be deployed across the various environments used in the development process, as well as the final production environments. This results in a more efficient, reliable, and predictable deployments. Solutions that automate your deployment processes improve the productivity of both the Dev and Ops teams and enable them and the business to develop faster, accomplish more, and ultimately build better software that is deployed more frequently and functions more reliably for the end-user.

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Infrastructure Automation

Infrastructure automation must start with strategy and a deep understanding of the process, which will inform automation choices. Configuration management tools should be used to automate infrastructure updates and scaling. Pair configuration management and infrastructure automation tools with a solid change management system, and your systems administrators might get an entire holiday off without worrying about alert calls. Yes, This can happen!

Fewer errors
Lower costs
Smarter approach

Security tools automation

Constant validation is an essential piece of security methodology and it takes place by way of continuous monitoring and alerting. A robust monitoring system helps us proactively detect issues and resolve them quickly.

Port availability monitoring
Centralized logging and analysis

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Automated threat detection

Automation leverages automation and machine learning so that it can be rapidly updated, retained, and applied to the constantly changing threat cyberscape. Building a machine learning pipeline from the ground up allows the organizations to learn directly from sample data, integrating it with other threat prevention platforms and doing some pattern mapping analysis so they can all benefit from classification-optimized algorithms.

This approach means systems can continually and dynamically learn what’s “normal” in software structure, software behavior, and network traffic patterns, usage thus becoming very effective. With machine learning, millions of variables and data points can be analyzed at once to identify anomalies that could indicate an attack.

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Threat response automation

Once we have security orchestration, we can trigger a chain of responses that can help mitigate the risk of the cyber threat spreading throughout the system or better still, prevent it.

Quality of data intelligence is a challenge. Cyber threat intelligence is often prone to false positives due to the obscure nature of IoT (Internet of Things). Threats can change instantly from one second to the next. Artificial intelligence and Machine learning will help us identify a group of steps that need to be executed based on threat detection. We can attain greater accuracy if there is pattern mapping from a global list of cyber threats or threat repository.

AI as a partner:

Informed Decision Making
Faster Resolution
Consistent and Stable Root Cause Analysis
Predictive analysis + Contingency execution

Security Automation + AI / ML is very relevant, this technology can learn from gradual training and failures which can easily and immediately identify any abnormal behavior. This statistically scores the priority of each potential threat that should be investigated. This improves the flag detection in real time and triggers necessary remediation steps.

The idea for AI in cybersecurity is to constantly adapt to the expanding threats in the cyberspace. Humans connecting the dots, distributing data and applying it to systems is a slow and ineffective process. A mature AI system can run through millions of data points, study threat repositories, connect the dots to improve the response time of contingencies to milliseconds.

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Conclusion – Artificial Intelligence + Automation is the future of cybersecurity.

Automation + AI solutions, in my opinion, are emerging as better partners for smarter, faster responses to ever-changing, threats and cyber attacks in today’s world. All technology and business leaders should take a look at this approach and implement this to better protect their infrastructure and data. They should tailor this approach to suit their requirements. This will truly help us with better defense and save the organizations a lot of pain and financial crisis.


Parisi, Alessandro. Hands-On Artificial Intelligence for Cybersecurity: Implement Smart AI Systems for Preventing Cyber Attacks and Detecting Threats and Network Anomalies. Packt Publishing Ltd, 2019.

Sipola, Tuomo, et al. Artificial Intelligence and Cybersecurity: Theory and Applications. Springer Nature, 2022.