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  1. AI Security

Artificial intelligence is defined as having machines do “smart” or “intelligent” things on their own without human guidance. As such, AI security involves leveraging AI to identify and stop cyber threats with less human intervention than is typically expected or needed with traditional security approaches.

AI security tools are often used to identify “good” versus “bad” by comparing the behaviours of entities across an environment to those in a similar environment. This process enables the system to automatically learn about and flag changes. Often called unsupervised learning or “pattern of life” learning, this method results in large numbers of false positives and negatives. More advanced applications of AI security can go beyond simply identifying good or bad behaviour by analysing vast amounts of information and helping to piece together related activity that could indicate suspicious behaviour. In this way, AI security behaves in a manner that’s similar to the best and most capable human analyst.

AI is shaping multiple aspects of security. Here we explain all aspects of AI security. However, the rest of the article will focus on AI in cybersecurity as this is the most common AI application in the security field today.

AI both presents opportunities for information/cybersecurity professionals to improve their cyber defences and new threats as cyber attackers leverage modern, publicly available machine learning algorithms.

AI security tools work to discover, predict, justify, act, and learn about potential cybersecurity threats, without needing much human intervention. Common AI security tool capabilities include:

  • “Learning” based on past behaviour to make quick, actionable context and insights when presented with new or unknown information/behaviours
  • Making logical, inferred conclusions based on potential incomplete subsets of data
  • Presenting multiple solutions to a known problem to empower security teams to select the best path towards remediation.

Increasing cyber security threats with Artificial Intelligence

Numerous companies are investigating the utilization of AI and Machine Learning to understand how to anchor their systems against cyber-attacks and malware. But given their tendency of self-learning, these AI frameworks have now also achieved a level where they can be trained to be a hazard to frameworks, i.e., go into all-out attack mode.

It is practically inevitable that we’ll see a boost in utilization for AI in our day to day life. However, much like different advancements in the tech market, this additionally opens up an entirely different road of grown misapplication from the cyber attackers. There is the definite probability for a level of misuse that develops past what we’ve seen up until this point, mainly as the tech keeps moving forward.

Let’s discuss how AI is affecting the cybersecurity negatively.

  • Hackers get an edge with AI

To begin with, it’s simply how similar advantages that cybersecurity specialists appreciate from the presentation of Artificial Intelligence are substantial for scammers and hackers too. Cybercriminals can utilize automation to impact the way toward finding new vulnerabilities which they can exploit quickly and easily.


Researchers and professionals are alerted about the danger AI technology models for cybersecurity, that generally critical practice that keeps our PCs and information and companies’ and governments’ PCs and information safe from cybercriminals.

According to a study by U.S. cybersecurity software titan Symantec  978 million individuals around 20 nations were influenced by cybercrime in 2017. Researchers said victims of cyber-attacks lost an aggregate of $172 billion, i.e., an average of $142 per individual as a result.

The dread for several is that AI will carry with it the beginning of new types of digital threats that sidestep common methods for countering attacks.

For instance, machine learning can create attacks scripts at a pace and level of multifaceted nature that mostly can’t be met by people.  Here are the further threats to cyber-security through AI.

  • Weaponizing drones and cars

The researchers from Cambridge and Oxford predicted that AI could be practiced to hack self-driving vehicles and drones, creating the likelihood of intentional car crashes and rogue bombings.

For instance, Google’s Waymo autonomous cars apply deep learning, and that system could be outwitted into reading a stop sign as a green light which will be causing a fatal accident.

Further, as the complexity of AI and cybercriminals increases, so will the odds of the real-time attacks. For instance, a few hackers can integrate drones into a swarm that may be fixed with explosives to carry out terror attacks or assassinations. It is Artificial Intelligence technology that empowers cybercriminals to program these attacks more effectively and to connect the drones with basic knowledge.

 Bot Hackers

We appreciate conversing with chatbots without acknowledging the amount of data we are transferring to them. Likewise, chatbots can be programmed to maintain talks with users in an approach to influence them into uncovering their financial or personal information, connections, etc.

In 2016, a Facebook bot spoke to itself as a friend and deceived 10,000 Facebook users into malware installation. Once the malware was imperilled, it seized the Facebook account of those users.

AI empowered botnets can debilitate HR through phone and online gateways support. The vast majority of us utilizing AI conversational bots, for example, Amazon’s Alexa or Google Assistant don’t comprehend the amount of knowledge they have about us. Being an IoT driven technology, they can generally hear, even the private discussions occurring around them. Additionally, some chatbots are poorly prepared for secure information transmissions, for example, Transport Level Authentication (TLA) or HTTPS protocols can be effectively utilized by hackers.

 Spear-phishing is becoming easy

Artificial Intelligence in security attacks will likewise make it less demanding for low-level cyber attackers to control complex interruptions by just computing with ease. Programmers regularly prevail by scaling their tasks. The more individuals they go after phishing plans or, the more systems they explore, the almost certain they are to get access. Artificial Intelligence furnishes them with an approach to scale to a lot higher level, through automation of the targets and delivering bulk attacks.

A fundamental precedent where cybercriminals are utilizing AI to dispatch an attack is through spear phishing. Artificial intelligence frameworks with the assistance of machine learning models can without much difficulty imitate people by creating swaying fake messages. Applying this technique, hackers can utilize them to perform more phishing attacks. Hackers can likewise utilize AI to make malware for deceiving programs or sandboxes that endeavour to find rogue code before it is sent in the system of an organization.

And AI-based phishing scams are just the inception. By utilizing machine learning, cyber-attackers could watch for possible vulnerabilities and automatize range of their possible victims. The similar technology could serve them in adequately analysing the AI-based cyber defines frameworks and produce new kinds of malware that could slide through them.

What’s more, AI can also certainly better customize the phishing plan by referencing all individual targets with that individual’s social data and other online data, making each attack bound to be profitable. Mainly from online networking websites like Twitter where the content is characterized by progressively casual vocabulary and grammar syntax use.

Utilizing a blend of histograms, natural language processing, and the scraping of publicly accessible information attackers will probably make increasingly tenable looking malicious emails while additionally diminishing the required burden and expanding the speed with which they can direct such cyber-attacks. The synchronous increment in the quality with the decrease in time, effort, and resources, implies that spear phishing will stay as amongst the most immovable cybersecurity issues of today.

 Malicious corruption

Organizations’ AI activities present a variety of potential vulnerabilities, incorporating malicious corruption or training data, usage, and segment configuration. No industry is resistant, and there are numerous classifications in which AI and machine learning as of now have a responsibility and in this manner present expanded threats. For instance, credit card scam may become simple.

Also, security, environment, and health systems may be imperilled that control cyber-physical gadgets which oversee train routing, traffic flow, or dam overflow.

 Social network mapping

Other AI-based risks will include high-level social networking mapping. For instance, AI-powered tools that will look further into social networking platforms will empower terrorists to detect the right city and human targets and operate all the more successfully.

Thus, cybersecurity professionals and defines departments should work unitedly to recognize such dangers and create solutions.

  • Home attacks

For numerous, parts of our personal lives are as of now automated with Biconnected gadgets and virtual assistants. But this requires a lot of personal information live in the cloud. This intricate system of connections will make new dimensions of vulnerability, with cyber threats hitting a lot nearer to home for instance, terrorists, rogue governments, and hackers could target Internet-linked medical gadget.

A long time from now, alarm systems and locks may not be sufficient to protect us at home. Rogue bots could persistently check systems, hunting down vulnerabilities. Along these lines, an alluring target is a helpless one. It implies anybody could turn into a potential target.

  • The Flip Side of AI

Fortunately, it’s not all dreary, and when the appropriate safety measures are taken, these vulnerabilities can be restricted. The malicious application of Artificial Intelligence likewise laid out proactively how the potential malevolent utilization of AI and Machine Learning can be moderated.

The concept is that cybersecurity organizations and researchers should cooperate in surveying the dangers and distinguish practices that can encourage security even though when facing AI-driven attacks. For a future where AI can turn into a focal element of regular life, so must be our emphasis on cybersecurity.

Using ai to improve Cyber security

Organizations leverage artificial intelligence to enhance their security against cyberattacks such as malware, phishing, network anomalies, unauthorized access of sensitive data. These tools use machine learning algorithms to learn from historical data and detect anomalies to enable organizations to prevent and manage cyberattacks effectively and efficiently. For example, AI powered deception technology helps delay and identify cyber attackers.

Defending against AI driven cyber attacks

Less than 90% of cybersecurity professionals in the US and Japan anticipating malicious AI-powered attacks. This is because AI research is publicly available and it can be used to build intelligent, continuously learning exploits by attackers.

For example, deep fakes are highly realistic videos, audio recordings, or photos generated by AI techniques. Some of their potential malicious uses include:

  • Overcoming biometric security systems
  • Infiltrating social networks
  • Using realistic video/audio/photos for manipulating users and gaining access to corporate networks/information.

AI-Powered physical security systems

Cameras record and transfer data to image recognition systems to identify threats (e.g. trespasser identification with cameras).

Securing AI systems against adversarial attacks

With AI technology, organizations have new processes such as data ingestion, preparation and labelling, model training, inference validation, and production deployment. These processes are new layers added to the organization’s tech processes that need to be protected from adversarial attacks. In adversarial attacks, attackers change the inputs of machine learning models to cause the model to make mistakes.

Since few deep learning systems are currently in production, adversarial attacks are still a mostly theoretical threat. Once deep learning systems start making important decisions, the importance of these threats will increase significantly. For example,

  • autonomous driving systems can be manipulated with subtle changes to road signs or their surroundings
  • industrial automation systems can similarly be manipulated for industrial sabotage


As an organization collects more data from different resources, potential points of cyberattack increases. According to a survey by Capgemini Research Institute, 69% of enterprises believe AI is necessary for cybersecurity due to increasing amount of threats that cybersecurity analysts can handle. Survey results show that 56% of the firms say their cybersecurity analysts are overwhelmed and 23% are not able to detect all breaches.

Another survey by TD Ameritrade, Registered Investment Advisors (RIA) are more willing to invest in new artificial intelligence cybersecurity ventures. With all these investment opportunities, AI security market forecasted to reach USD 38 billion by 2026 from USD 8 billion in 2019, at CAGR of 23.3%

The Impact of AI on Cyber Security

There is currently a big debate raging about whether Artificial Intelligence (AI) is a good or bad thing in terms of its impact on human life. With more and more enterprises using AI for their needs, it’s time to analyse the possible impacts of the implementation of AI in the cyber security field.

  • The positive uses of AI for cyber security

Biometric logins are increasingly being used to create secure logins by either scanning fingerprints, retinas, or palm prints. This can be used alone or in conjunction with a password and is already being used in most new smartphones. Large companies have been the victims of security breaches which compromised email addresses, personal information, and passwords. Cyber security experts have reiterated on multiple occasions that passwords are extremely vulnerable to cube attacks, compromising personal information, credit card information, and social security numbers. These are all reasons why biometric logins are a positive AI contribution to cyber security.

AI can also be used to detect threats and other potentially malicious activities. Conventional systems simply cannot keep up with the sheer number of malwares that is created every month, so this is a potential area for AI to step in and address this problem. Cyber security companies are teaching AI systems to detect viruses and malware by using complex algorithms so AI can then run pattern recognition in software. AI systems can be trained to identify even the smallest behaviours of ransomware and malware attacks before it enters the system and then isolate them from that system. They can also use predictive functions that surpass the speed of traditional approaches.

Systems that run on AI unlock potential for natural language processing which collects information automatically by combing through articles, news, and studies on cyber threats. This information can give insight into anomalies, cyber-attacks, and prevention strategies. This allows cyber security firms to stay updated on the latest risks and time frames and build responsive strategies to keep organizations protected.

AI systems can also be used in situations of multi-factor authentication to provide access to their users. Different users of a company have different levels of authentication privileges which also depend on the location from which they’re accessing the data. When AI is used, the authentication framework can be a lot more dynamic and real-time and it can modify access privileges based on the network and location of the user. Multi-factor authentication collects user information to understand the behaviour of this person and make a determination about the user’s access privileges.

To use AI to its fullest capabilities, it’s important that it’s implemented by the right cyber security firms who are familiar with its functioning. Whereas in the past, malware attacks could occur without leaving any indication on which weakness it exploited, AI can step in to protect the cyber security firms and their clients from attacks even when there are multiple skilled attacks occurring.

Drawbacks and limitations of using AI for cyber security

  • The benefits outlined above are just a fraction of the potential of AI in helping cyber security, but there are also limitations which are preventing AI from becoming a mainstream tool used in the field. In order to build and maintain and AI system, companies would require an immense amount of resources including memory, data, and computing power. Additionally, because AI systems are trained through learning data sets, cyber security firms need to get their hands on many different data sets of malware codes, non-malicious codes, and anomalies. Obtaining all of these accurate data sets can take a really long time and resources which some companies cannot afford.
  • Another drawback is that hackers can also use AI themselves to test their malware and improve and enhance it to potentially become AI-proof. In fact, an AI-proof malware can be extremely destructive as they can learn from existing AI tools and develop more advanced attacks to be able to penetrate traditional cyber security programs or even AI-boosted systems.

Solutions to AI limitations

Knowing these limitations and drawbacks, it’s obvious that AI is a long way from becoming the only cyber security solution. The best approach in the meantime would be to combine traditional techniques with AI tools, so organizations should keep these solutions in mind when developing their cyber security strategy:

  • Employ a cyber security firm with professionals who have experience and skills in many different facets of cyber security.
  • Have your cyber security team test your systems and networks for any potential gaps and fix them immediately.
  • Use filters for URLs to block malicious links that potentially have a virus or malware.
  • Install firewalls and other malware scanners to protect your systems and have these constantly updated to match redesigned malware.
  • Monitor your outgoing traffic and apply exit filters to restrict this type of traffic.
  • Constantly review the latest cyber threats and security protocols to get information about which risks you should be managing first and develop your security protocol accordingly.
  • Perform regular audits of both hardware and software to make sure your systems are healthy and working.

Following these steps can help mitigate many of the risks associated with cyber-attacks, but it’s important to know that your organization is still at risk of an attack. Because of this, prevention is not enough and you should also work with your cyber security team to develop a recovery strategy.

Leading Companies for AI Security use cases

E-mail monitoring: E-mail is a common target for cyber threats. AI monitoring software helps improve the detection accuracy and the speed of identifying cyber threats.

  • Tessian

Network threat analysis and Malware Detection: Organizations use AI to identify malicious malware and the differences between real and artificial users to prevent fraud access.

  • LogRhythm
  • Spark Cognition
  • Cylance
  • White Ops
  • Versive
  • Cybereason
  • Cylance
  • Anomali
  • Fortinet
  • Palo Alto Networks
  • Shape Security
  • Cujo AI

AI against AI-based threats: Hackers are using AI as well. Organizations need AI to prevent an organization from AI-based threats.

  • Check Point

AI to automate repetitive security tasks: Organizations leverage AI to automate repetitive tasks of security analysts so that they can shift their focus on more important tasks. 

  • Vectra

Fortinet and Palo Alto Networks are the two leading AI security companies that generate 1.8 billion USD and 2.27 billion USD respectively.

If you want to improve the security of your organization but don’t know where to start, here are a few pieces of our research about cybersecurity:

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