Breaches are getting worse. They are creating more risk, danger, and impact than ever before. Your business can no longer afford to suffer the consequences of a successful breach, and your legacy cybersecurity solutions can no longer prevent modern attacks. Today, you can only prevent and respond to hacks and breaches when you add artificial intelligence (AI) to your cybersecurity activities.
On this page, we’re going to show you how AI and cybersecurity work together, We will illustrate how the benefits of artificial intelligence can enhance your cybersecurity posture. And we will explain to you:

  • Why you must augment your cybersecurity efforts with added AI
  • How AI and your cybersecurity efforts will pair together
  • Whether or not AI should replace your human cybersecurity teams
  • Which cybersecurity activities need AI the most
  • And much more…

 

 

Index

Breaches are getting worse. They are creating more risk, danger, and impact than ever before. Your business can no longer afford to suffer the consequences of a successful breach, and your legacy cybersecurity solutions can no longer prevent modern attacks.

 

Today, you can only prevent and respond to hacks and breaches when you add artificial intelligence (AI) to your cybersecurity activities.


On this page, we’re going to show you how AI and cybersecurity work together, We will illustrate how the benefits of artificial intelligence can enhance your cybersecurity posture. And we will explain to you:

  • Why you must augment your cybersecurity efforts with added AI
  • How AI and your cybersecurity efforts will pair together
  • Whether or not AI should replace your human cybersecurity teams
  • Which cybersecurity activities need AI the most
  • And much more…

Why Use AI in Cybersecurity?

More Threats

The threat landscape has changed. Organizations have more vulnerability points than ever—and these openings will only grow as organizations continue to embrace the cloud, mobile, and the Internet of Things (IoT). Cybercriminals are taking advantage of these vulnerability points by developing and launching sophisticated, high-volume, multi-dimensional attacks. These attacks are producing a flood of threat data, and organizations now face thousands of alerts at a time, finding themselves forced to spend valuable, scarce time analyzing hundreds of thousands of potentially malicious files every day just to avoid breaches.

More Complex Challenges

This increasingly-complex, data-flooded threat landscape has created new challenges that render traditional cybersecurity obsolete. Attacks and attackers are no longer known, and can’t be prevented— they must be uncovered and shut down in near real-time. Leading organizations have recognized this hard truth, and come to see that they cannot prevent breaches anymore, and must instead begin to focus on continuously monitoring their systems, detecting threats, and responding in near-real-time. They realize they can only stop the modern cybercriminal's stealthy, sophisticated, multi-channel Advanced Persistent Threats, if they develop the ability to search through every point of vulnerability within every one of their organizational systems at all times, and at every step of every threat’s lifecycle.

“Attacks and attackers are no longer known, and can’t be prevented— they must be uncovered and shut down in near real-time.”

The Solution: AI Software Defenses

This is an impossible task to complete if you still only use legacy, manual cybersecurity practices. You can only contend with this flood of threat data, and stop these advanced attacks, if you layer big-data driven, AI defenses into your security posture.

 

The right artificial intelligence software will give you the power to process a near-limitless volume of threat data, and to effectively prevent and respond to hacks and breaches in the world of today’s deadly cybersecurity.

 

Here’s how…

 

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First Thing’s First: What Is Artificial Intelligence, Really?

“Artificial intelligence” is more than a fluffy buzzword. Properly developed and deployed, it offers the key to keeping your business’s information and systems safe. But given the hype and misinformation around the topic, it’s worth taking a minute to discuss what AI is exactly, and what benefits and capabilities it adds
to cybersecurity.

What AI Isn’t

AI isn’t a super-intelligent machine with incredible general intelligence that will replace every member of your cybersecurity team. (More on this in a minute.) This sort of “general AI” is the stuff of science fiction, and bears little resemblance to reality.

 

AI also is not exactly the same thing as “machine learning”, “data science”, and other terms that it is commonly (and inaccurately) used interchangeably with. For example:

  • Machine Learning – This is a subset of AI, but does not encompass everything that’s involved in AI, nor is it the most important element of AI. It is simply an approach concerned with how machines build knowledge. For example, a machine can codify rules that experts know, and repeat them. Machines can also break these codified rules and better model our complex world by “learning” from past organizational data to create their own knowledge.
  • Data Science – This is a broad topic that can include AI, and it involves all the activities required to drive machine learning, but it can include a whole lot of other activities and approaches as well. Data science can refer to any process, framework, or activity involved in defining datasets, choosing appropriate variables and metrics, and carrying out various tasks of data engineering that include, but are not limited to: data collection, preparation, integration, visualization, and measuring of algorithm performance.

Now that you have a sense of what AI is not, we’re ready to take a look at what AI actually is.

What It Is

At the most basic level, artificial intelligence is exactly what it sounds like: intelligence that comes from something artificial–a machine. AI can encompass activites that include creating machines that can mimic humans, or otherwise creating machines that can understand and respond with intelligent capabilities. In order for an artificially intelligent machine to mimic humans, they need two things: knowledge and action. So AI, roughly speaking, is just about creating machines that are able to absorb or create knowledge, and to then take action on that knowledge. These machines, this knowledge, and these actions, can apply to virtually any domain. But they are particularly powerful when they are applied to cybersecurity.

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Application of AI to Cybersecurity

How does AI work with cybersecurity?

The right AI technology will enhance every level of your cybersecurity system. It will increase the speed and accuracy of your predictions, system monitoring, threat detection, and incident response activities by first collecting and processing a staggering volume of raw data in order to uncover anomalies that speak to an attack, and then, second, by automating the appropriate actions to eliminate that threat ASAP.

Let’s dig into how AI does all this.

Recognizing and Discovering Attacks

Every attack—even an unknown attack—leaves a network event trail. Properly uncovered and analyzed, these anomalies show you the steps an attacker has taken within your network. They can tell you how the attacker breached your systems, where they have been, where they are likely going, and what their plausible aim might be. Uncovering and analyzing this network event trail essentially turns an unknown attack into a known attack—one you can effectively respond to, and one you can prevent in the future.

 

This is a powerful approach, but it has one problem— uncover and analyze this network event trail, you must collect, analyze, contextualize and process every piece of raw data produced by your network.

Processing Power

Modern cyberattacks take advantage of a huge range and variety of vulnerability points to breach their targets. And once they breach your perimeter, they show up with many different behaviors as they move through many files, networks, protocols, and systems to reach their target. In essence— An anomaly can appear anywhere, and any anomaly can indicate a breach. You will only be able to find these threats if you can analyze and act on every single piece of data that moves through your network’s flows, forensics, and logs. And you will only be able to process the volume of data required to find them, analyze them, and raise the red flag by deploying an a AI-driven big-data-driven platform.

 

Human intelligence just can’t process all of this data fast enough to spot these clever modern attacks. But that does not mean that human intelligence is no longer an important element of cybersecurity.

“An anomaly can appear anywhere, and any anomaly can indicate a breach. You will only be able to find these threats if you can analyze and act on every single piece of data that moves through your network’s flows, forensics, and logs.”

Does Cybersecurity Still Require Human
Intelligence (HUMINT)?

AI can’t do everything. AI and humans have different forms of intelligence, and both are required to effectively perform modern cybersecurity. What human intelligence lacks in calculation, we more than makeup for in other aspects of cognition.

 

It’s true that humans didn’t evolve to do a large number of calculations fast. Instead, we evolved the ability to reason, hypothesize, explore, deduce and predict - and we evolved to perform each of these cognitive tasks under ambiguity and with insufficient data. Our brains are complex biological computers that can perform certain cognitive tasks that even the fastest synthetic modern supercomputer can’t simulate.

 

Every cybersecurity expert will tell you the ability to make reasoned, intuitive decisions - with lots of ambiguity thrown in - is critical to detect and respond to threats. In cybersecurity, when you are trying to evaluate a risk or making a judgment on an alert, or determining an appropriate response, you need these aspects of Human Intelligence. And current AI technologies haven’t yet evolved the ability to replicate such capabilities of
human intelligence.

 

What current AI technologies can do is to bring fast mathematical calculations to augment these critical capabilities of human intelligence. And this is the area of application where AI produces the greatest benefit for cybersecurity, what we call AI augmentation.

 

AI works best when it augments human intelligence - not replaces it - in a few key areas where machine intelligence simply performs better, and, quite frankly, is now required. Here’s where.

“AI works best when it augments human intelligence -
not replaces it”

AI Augmentation: What Is It and
Why Is It Useful?

There are many cybersecurity actions that AI can’t complete itself, but which AI can assist a human to perform faster, more accurately, and overall more effectively. For example, AI might not be able to decide if an alert is an actual attack, as those sorts of tasks require a human’s broad cognition skills. But AI can hasten the detection of an attack by augmenting a human analyst’s ability to make that call. AI can present potential threats to human analysts, answer questions analysts throw at it, prove or disprove a hypothesis from a human analyst, and execute tasks that human analysts have ratified.

 

Let’s take a more detailed look at some of the key areas where AI can augment and dramatically improve the performance of human cybersecurity teams and experts.

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Most Effective Types of AI Augmentation
in Cybersecurity

Triaging

All rule-based detection systems suffer from the problem of false positives. This is not a problem of poorly designed or engineered products. It’s a problem within the inherent logic of cybersecurity.

 

Attacks are few and far between. But in our domain, there is a heavy penalty for producing a false negative. If an attack happens and the product fails to detect it, the consequences are heavy. So, every security product tries to make false negatives as close to zero as possible by alerting every potential attack. The consequent side effect is that the false positives rises. If you absolutely don’t want to miss a wolf, you will need to cry wolf at every
possible opportunity.

 

This deluge of mostly false alerts overwhelms human analysts. In the face of such a large number of alerts, analysts in a Security Operations Center (SOC) end up creating some rules of thumb for triaging these alerts. And then do detailed analysis on such filtered alerts. Other alerts are dropped in this process. This approach is not very effective given the nature of advanced threats today. That harmless-looking alert could be the real attack.

 

AI software can be used to augment human analysts here. AI can deploy machine learning methods of historical patterns, clustering, association rules, and data visualization to quickly filter out the most relevant alerts, and present only these triaged and enriched alerts for human analysts to investigate further.

Threat Hunting

Another inherent problem within cybersecurity is that it is asymmetric. A cyber attacker needs to only be successful once, by exploiting just one weakness. While we, the defenders, must be successful every time. To do so, we need to comb for threats on the entire IT stack - not just security data. AI is extremely useful here as it can look for patterns, anomalies, and outliers in all of this data without the need for fixed rules, and then present the output to human analysts for investigation. (In security language, this is called threat hunting - when you narrow down threats by combining security analytics with machine intelligence and advanced cognition of humans.)

Incident Analysis/ Investigation

Humans have a natural advantage when it comes to investigating a potential incident and deciphering the complete attack chain. These investigations require a lot of reasoning skills that current AI methods lack. To investigate, you have to constantly ask new questions, form new hypotheses, and collect more evidence to confirm or reject those hypotheses. Machines can mine vast amounts of data to provide answers, but they can’t pose questions as effectively and iteratively as humans.

 

This is the classical exploitation versus exploration challenge. Machines can perform lots of data exploitation, but humans are needed to perform lots of exploration. To investigate a cyber alert or incident and form the attack’s story, you need to combine both strong reasoning with the large-scale capability to collect and mine past data.

In this activity, AI models primarily answer:

  • What happened to the asset (impact)
  • Who the attackers are (with their attributes)
  • What the past sequences were in the attack chain on that asset
  • What is the blast radius (which other assets are part of the attack)
  • Who is the “patient zero” (where the attack originated).

Threat Anticipation

AI software can also augment Human capabilities during threat anticipation. Threat anticipation allows you to anticipate what could hit you next, based on what is happening elsewhere in the world. It identifies when a breach happens to another company, and ensures you quickly learn about it, extract the relevant threat intel, and apply that information in your environment.

 

Today the first step in threat anticipation - automating the collection of machine-readable threat intel data - is already being done on a large scale. But AI techniques can also be used to increase the accuracy and fidelity when applying this data to each organization’s unique context. When it comes to mining human-readable threat data—such as blogs, forums, social media, and dark web source - AI techniques such as text analytics and natural language processing can help to identify the most relevant data that a human threat analyst should read. AI techniques can group and categorize this unstructured data automatically along topics and semantics. Human threat analysts then avoid wasting time reading through a large daily volume of unstructured data and focus on applying the relevant actions in each organization’s context.

Incident Response

AI also assists in incident response. Once an alert has been confirmed as an incident, an effective response requires four major steps:

  • Containing the spread
  • Recovering the affected systems
  • Mitigating the root causes of the attack, and
  • Improving your security posture for the future.

At each of these stages, incident responders need to know what to do and how to automate that step. AI techniques - such as knowledge engineering and case-based reasoning - can be used to create playbooks to guide incident responders in this what-to-do phase. These playbooks are built by machines based on previous incidents, and also incorporate codified knowledge from human experts. The AI thus learns with each new incident, and continuously modifies or creates branches of the main playbook. Incident responders then use these playbooks to execute faster actions, while using their own deeper knowledge of organizational context to ensure the right response.

 

These are only a few of the most critical ways that AI can augment human cybersecurity experts and teams, and deliver superior outcomes. In each case, the core factor that AI brings to the table is a high level of automated activity that humans can’t replicate on their own - no matter how large of a team they put together.

 

Let’s take a deeper look at the role of AI-drive. automation in cybersecurity.

“AI learns with each new incident, and continuously modifies or creates branches of the main playbook.”

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Cybersecurity Autonomics

A generic autonomic system not only processes an input into output based on rules but also pulls in data from a variety of other sensors, makes judgments based on those sensory data with a repository of knowledge, and keeps learning with experience.

Autonomics in security operations can exist at 3 levels:

Level 1: Using Sensory Data and Adding Decision Making
to Automation

At this level, it’s all about having full contextual information on:

  • Assets
  • Vulnerabilities
  • Attackers
  • Network
  • Controls

Once this information is acquired, historical data is used along with context to arrive at a score for each alert. The scoring is the know-how part which will vary with each organization, but can also be built into a platform.

 

So, a platform that can constantly collect a variety of context data and has a model for scoring can do the job of alert triaging and only the triaged alert will go to an analyst. In this model, machines can do thousands of alert analyses without the need for human involvement.

“Machines can do thousands of alert analyses without the need for human involvement.”

Level 2: Models and Algorithms for Decision Making Beyond a Rule-based System

Scoring an alert based on a variety of sensory data and historical data is a simple decision-making system. Complexity arises when the context data is incomplete and a machine needs to extrapolate or predict it. For instance, if there is vulnerability data that is not available for some assets under attack and a machine needs to predict the vulnerabilities based on other similar assets (profiling) or patching history (Bayesian probabilistic models).

Similarly, for hunting, the machine executes a data-sciences-driven model based on triggers (malware beaconing based on a trigger of AV alert) and a hunter only looks at the output of
such models.

Level 3: Learning from Experience

The Holy Grail for machine learning in security is to immediately know from alerts that an attack is actually happening or has succeeded, and then to take the right countermeasure to stop or eradicate it. This level of autonomics may not be possible in security where there are many factors that make an alert qualify as an attack.

 

Unfortunately, there are too many contextual parameters for modeling reliable and consistent supervised machine learning. However, the sub-tasks can be modeled for a machine to learn from human analysts. For example, for a certain type of alert, the analysts might pull a specific set of data from packets, logs, and/or end machines and carry out a certain type of analysis. The hope is that the machine can learn to do this analysis automatically in the future and present it to human analysts. Similar things can be done for
counter-measure deployments

Your Most Secure Option

By now, it should be clear that AI’s applications in cybersecurity are critical— but they are also complex, they are highly-specialized, and they are near-impossible for any organization to leverage on their own. Any organization that struggles to complete the fundamental day-to-day activities of basic cybersecurity will face a high degree of challenging creating their own AI-driven cybersecurity platform, integrating that platform into their existing organizational systems, and constantly improving the performance and outcomes delivered by that system.

 

This creates a bit of a dilemma. Clearly, artificial intelligence software is able to enhance cybersecurity in a myriad of ways. And the best cybersecurity options should be using it. But it’s near impossible to deploy on your own.

 

There is only one effective solution. You must find the right partner to bring AI to your cybersecurity efforts.

 

At Paladion, we use AI to provide Managed Detection and Response (MDR) services. This is unlike traditional MSSPs who only provide alerts from security monitoring. Using advanced security analytics on endpoints, user behavior, application, and network; MDR provides deeper detection compared to traditional MSSPs, who mostly rely on rules and signature. For faster response, MDR also uses AI and machine learning to investigate, auto contain threats, and orchestrate response.

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You'd be surprised how effective it can be to just include a warm, smiling photograph of yourself. Or maybe you wouldn't be so surprised. Maybe you just don't surprise easily. Or maybe you're just incredibly wise. Yes. That must be it. Wise and warmly smiling. What a winning combination.
Shawn Bristow

Shawn Bristow HubSpot, Inc.