Artificial Intelligence (AI) In Cyber Security

What is AI Security?

AI security refers to tools and techniques that leverage AI (AI) to autonomously identify and/or respond to potential cyber threats based on similar or previous activity. 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 spot and stop cyber threats with less human intervention than is usually expected or needed with traditional security approaches. 

AI security tools are often used to identify “good” versus “bad” by comparing the behaviors 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 far simply identifying good or bad behavior by analyzing vast amounts of data and helping to piece together a related activity that would indicate suspicious behavior. in this way, AI security behaves in a manner that’s just like the simplest and most capable human analyst.

Common Usage and Adoption

AI security tools work to find, predict, justify, act, and learn about potential cybersecurity threats, with no need for much human intervention. Common AI security tool capabilities include:-

Learning supported past behavior to create quick, actionable context and insights when presented with new or unknown information/behaviors. Making logical, inferred conclusions based on potential incomplete subsets of knowledge. Presenting multiple solutions to a known problem to empower security teams to pick the simplest path towards remediation.

AI Security Augments the Shrinking Cyber Workforce

Resourcing has historically been a challenge in many SOCs. When it involves manpower alone, the cybersecurity industry’s projected talent gap is anticipated to examine 3.5 million unfilled jobs by 2021. While some argue that AI machines can or will fill this gap, a more scalable solution is to adopt AI security tools that augment the workflows of existing employees. this will greatly release sparse resources by lowering on time needed for threat hunting and alert triage or correlation, as an example. Cybersecurity workers are then able to concentrate on other important tasks that can't be automated through AI.

Quantum Technology

One of our most significant technical aids for protecting information is encryption. Using encryption, information can be hidden, fidelity proven and private identity verified. A challenge that many are presently studying is how encryption should be adapted for a world where we've many connected devices that will not have all that much calculative capacity, memory, and electrical power. this is a serious challenge but many have taken it on and therefore the technology will be developed soon. 

An interesting development that has got to be considered if we look 10 to 20 years into the longer term is how quantum technology will affect the encryption methods we use. There are two different encryption methods, symmetric and asymmetric. Symmetric encryption is going to be weakened by quantum computers, but with longer encryption keys, it can still be used. The algorithms presently used for asymmetric encryption will, however, lose all their security and it's these asymmetric encryption algorithms that much of today's e-commerce, e-identities and certification management are supported. Here it's important to follow development so that these algorithms can be replaced before quantum computers become reality.

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Benefits of AI In Cyber Security

1. AI can handle the quantity - AI automates the method of detecting advanced threats. AI can analyze the very large volume of activity that takes place across a company’s network and therefore the massive volume of emails, files, and websites accessed by employees during a small fraction of the time needed by humans. While AI isn't 100% accurate in detecting threats, it can identify the vast majority of activity and samples that are benign, allowing its human counterparts to concentrate on the relatively small number of the suspicious, potentially malicious remainder.
2. AI cybersecurity can learn over time - AI can identify malicious attacks based on the behaviors of applications and also the behavior of the network as a whole. Over time, AI cybersecurity solutions learn about a network’s regular traffic and behaviors and can spot deviations from the norm.
3. AI identifies unknown threats - thousands of millions of malicious attacks are launched each year. Cybersecurity professionals often find themselves playing catch up with these threats, which are frequently a step ahead. Since network security solutions that use AI don’t believe signatures, they can spot zero-day attacks.

Limitations of AI In Cyber Security

1. Cyber threats are constantly evolving - Bad actors are creative and have virtually unlimited resources; in some areas, cybercrime is an economy unto itself. As new threats emerge, security solutions that use AI need to be re-trained to keep up.
2. Cybercriminals use AI - They're able to acquire AI-driven cybersecurity solutions and test their malicious programs against them. As a result, they can theoretically create an AI proof malware strain. They also use machine learning to know what AI-based security systems are looking for and then can either disguise their attack or pollute the sample so their attack appears to be benign. Security is the only field where AI systems fight back.
3. It’s better to take caution - AI systems aren't yet advanced enough to be 100% accurate in distinguishing between malicious and benign activity. to protect a network and its applications and data, most cybersecurity solutions – including AI-based solutions – err on the side of caution. That is when in doubt, flag something as anomalous and potentially threatening. This creates alerts about anomalous activities that need to be investigated by human analysts and turn out to be benign. the alternative to being cautious to reduce the amount of false positives risks missing real attacks.