Managed AI Threat Hunting
How does artificial intelligence interact with cybersecurity?
The right AI technology will bolster your cybersecurity system at every level. It accelerates and improves the accuracy of your predictions, system monitoring, threat detection, and incident response activities by first collecting and processing a massive amount of raw data in order to identify anomalies indicative of an attack, and then automating the necessary actions to eliminate the threat as quickly as possible.
What to Know
Breach rates are increasing. They are posing greater risk, danger, and impact than at any point in history. Your business cannot afford the costs associated with a successful breach, and your legacy cybersecurity solutions are no longer capable of preventing modern attacks.
At the moment, the only way to prevent and respond to cyberattacks and breaches is to incorporate artificial intelligence (AI) into your cybersecurity activities.
On this page, we’ll demonstrate how artificial intelligence and cybersecurity work in tandem. We’ll demonstrate how the benefits of artificial intelligence can help you improve your cybersecurity posture. And we’ll explain:
- Why you must augment your cybersecurity efforts with additional artificial intelligence
- How artificial intelligence and your cybersecurity efforts will complement one another
- Whether or not to replace human cybersecurity teams with AI
- Which cybersecurity activities are most in need of AI?
Why Should Artificial Intelligence Be Used in Cybersecurity?
Additional Threats – The threat environment has shifted. Organizations now have more vulnerability points than ever before—and these gaps will continue to grow as organizations embrace cloud, mobile, and the Internet of Things (IoT). Cybercriminals exploit these points of weakness by developing and launching sophisticated, high-volume, multi-dimensional attacks. These attacks generate a flood of threat data, and organizations are now forced to spend valuable, scarce time each day analyzing hundreds of thousands of potentially malicious files in order to avoid breaches.
More Difficult Tasks – This increasingly complex, data-rich threat landscape has created new challenges that have rendered traditional cybersecurity ineffective. Attacks and attackers are unknown and cannot be prevented; they must be uncovered and neutralized in near real time. Leading organizations have accepted this harsh reality and realized that they can no longer prevent breaches and must instead focus on continuously monitoring their systems, detecting threats, and responding in near-real time. They recognize that they can only defeat modern cybercriminals’ stealthy, sophisticated, multi-channel Advanced Persistent Threats if they develop the capability to search through every point of vulnerability within each of their organizational systems at all times and throughout each threat’s lifecycle.
Software Defense with AI
This is an insurmountable task if you continue to rely on manual cybersecurity practices. Only by incorporating big-data-driven, AI-based defenses into your security posture will you be able to deal with this flood of threat data and thwart these advanced attacks.
The right artificial intelligence software will enable you to process an almost infinite volume of threat data, as well as to effectively prevent and respond to hacks and breaches in today’s lethal cybersecurity environment.
What Exactly Is Artificial Intelligence?
“Artificial intelligence” is more than a catchphrase. Properly developed and deployed, it is critical in ensuring the security of your business’s information and systems. However, given the hype and misinformation surrounding the subject, it’s worth taking a moment to define AI and the benefits and capabilities it brings to cybersecurity.
Myths about AI
- AI is not a superintelligent machine capable of superhuman general intelligence that will eventually replace every member of your cybersecurity team. This type of “general AI” is a fantasy that bears little resemblance to reality.
- Additionally, AI is not synonymous with “machine learning,” “data science,” and other terms that are frequently (and incorrectly) used interchangeably with it. For instance:
- Machine Learning – This is a subset of AI, but it does not encompass all aspects of AI, nor is it the most critical component. It is simply a method for examining how machines acquire knowledge. For instance, a machine can codify and repeat rules that only experts understand. Machines can also deviate from these codified rules and create a more accurate model of our complex world by “learning” from previous organizational data.
- Data Science – This is a broad topic that includes AI and encompasses all of the activities necessary to drive machine learning, but it also encompasses a plethora of other activities and approaches. Data science can refer to any process, framework, or activity that involves the definition of datasets, the selection of appropriate variables and metrics, and the execution of various data engineering tasks, such as data collection, preparation, integration, visualization, and algorithm performance measurement.
So what is it?
At its most fundamental level, artificial intelligence is precisely what it sounds like: intelligence derived from an artificial entity–a machine. AI can encompass activities such as developing machines that can mimic humans or developing machines that are capable of understanding and responding intelligently. To mimic humans, an artificially intelligent machine requires two things: knowledge and action. Thus, AI is essentially about developing machines that are capable of absorbing or creating knowledge and then acting on it. This knowledge, these machines, and these actions are applicable to virtually any domain. However, they are particularly effective when applied to cybersecurity.
Recognize and Detect Attacks
Every attack, even those that are unknown, leaves a network event trail. When discovered and analyzed properly, these anomalies reveal the path an attacker took within your network. They can provide information about how the attacker gained access to your systems, where they have been, where they are likely to go, and what their plausible objective may be. By identifying and analyzing this network event trail, an unknown attack becomes a known attack—one to which you can respond effectively and prevent in the future.
This is a powerful approach, but it has one drawback—in order to uncover and analyze this network event trail, you must collect, analyze, contextualize, and process all raw data generated by your network.
Is Human Intelligence Still Required for Cybersecurity?
AI is not capable of performing all tasks. While AI and humans have distinct intelligences, both are required to perform modern cybersecurity effectively. Where human intelligence falls short in calculation, we make up for it in other areas of cognition.
True, humans did not evolve to perform large numbers of calculations quickly. Rather than that, we evolved the ability to reason, hypothesize, explore, deduce, and predict – and to do so in the face of ambiguity and insufficient data. Our brains are sophisticated biological computers capable of performing cognitive tasks that even the fastest modern supercomputer cannot replicate.
Every cybersecurity expert will tell you that the ability to make rational, intuitive decisions – with a healthy dose of ambiguity – is critical for detecting and responding to threats. When attempting to evaluate a risk, make a judgment about an alert, or determine an appropriate response in cybersecurity, these aspects of Human Intelligence are required. And current AI technologies have not evolved to the point where they are capable of replicating such capabilities.
What current AI technologies can do is augment these critical capabilities of human intelligence with fast mathematical calculations. And this is the application area in which AI has the greatest benefit for cybersecurity, which we refer to as AI augmentation.
AI works best when it augments – not replaces – human intelligence in a few critical areas where machine intelligence simply outperforms human intelligence and, quite frankly, is now required.
Effective Methods of Artificial Intelligence Augmentation in Cybersecurity
Triaging – False positives are a problem that plagues all rule-based detection systems. This is not a problem caused by substandard design or engineering. This is a problem inherent in the logic of cybersecurity. Attacks are rare and infrequent. However, there is a severe penalty for producing a false negative in our domain. If an attack occurs and the product is unable to detect it, the repercussions are severe. As a result, every security product strives to keep false negatives to a minimum by alerting to every potential attack. As a result, the number of false positives increases. If you absolutely cannot afford to miss a wolf, you must cry wolf at every opportunity.
Threat Hunting – Human analysts are overwhelmed by this deluge of mostly erroneous alerts. Faced with such a high volume of alerts, analysts in a Security Operations Center (SOC) are forced to develop some rules of thumb for triaging these alerts. And then perform in-depth analysis on these filtered alerts. During this process, additional alerts are suppressed. This strategy is ineffective in light of the nature of advanced threats today. That seemingly innocuous alert could actually be the source of the attack. Here, artificial intelligence software can be used to supplement human analysts. AI can use machine learning techniques such as historical patterns, clustering, association rules, and data visualization to rapidly filter out the most pertinent alerts and present only the triaged and enriched alerts to human analysts for further investigation.
Threat Identification – Another inherent weakness of cybersecurity is its asymmetric nature. A cyber attacker needs to be successful only once, by exploiting a single vulnerability. While we, as defenders, must be successful on a consistent basis. To accomplish this, we must scour the entire IT stack for threats, not just security data. AI is extremely beneficial in this situation because it can search for patterns, anomalies, and outliers in all of this data without relying on fixed rules and then present the output to human analysts for further investigation. (In security parlance, this is referred to as threat hunting – the process of narrowing down threats through the use of security analytics, machine intelligence, and advanced human cognition.)
Analysis/Investigation of an Incident – Humans are predisposed to investigate potential incidents and decipher the entire attack chain. These investigations necessitate a high level of reasoning ability, which is lacking in current AI methods. To conduct an investigation, you must constantly pose new questions, form new hypotheses, and gather additional evidence to support or refute those hypotheses. While machines are capable of mining massive amounts of data in order to provide answers, they cannot pose questions as effectively or iteratively as humans can.
AI primarily responds to the following:
- What happened the asset?
- Who the perpetrators are
- What were the previous sequences in the attack chain against that asset?
- What is the radius of the explosion?
- Who is “patient 0”?
- Anticipation of Threat
Additionally, AI software can augment human capabilities when it comes to threat anticipation. Threat anticipation enables you to forecast what may happen next based on events occurring elsewhere in the world. It detects when another company suffers a breach and ensures that you are notified immediately, extract pertinent threat intelligence, and apply it to your environment.
Currently, the first step in threat anticipation – automating the collection of machine-readable threat intelligence – is being carried out on a large scale. However, AI techniques can be used to improve the accuracy and fidelity of this data when applied to each organization’s unique context. When mining human-readable threat data – such as that found on blogs, forums, social media, and dark web sources – AI techniques such as text analytics and natural language processing can assist in identifying the most pertinent data for a human threat analyst to read. Artificial intelligence techniques can automatically group and categorize this unstructured data according to topics and semantics. Human threat analysts can then avoid wasting time poring over a large daily volume of unstructured data and instead focus on implementing the appropriate actions in the context of each organization.
- Response to an Incident – additionally, AI aides in incident response. After an alert is confirmed to be an incident, an effective response entails four critical steps:
- Keeping the spread in check
- Restoring the compromised systems
- Identifying and mitigating the attack’s underlying causes, and
- Future-proofing your security posture.
Each of these stages requires incident responders to understand what to do and how to automate the process. Artificial intelligence techniques such as knowledge engineering and case-based reasoning can be used to develop playbooks that guide incident responders through this what-to-do phase. These playbooks are generated by machines using historical data and also include codified knowledge from human experts. Thus, the AI continuously learns from new incidents and modifies or creates branches of the main playbook. Incident responders then use these playbooks to execute actions more quickly, while still relying on their own in-depth knowledge of the organizational context to ensure the appropriate response.
These are just a few of the critical ways in which AI can assist human cybersecurity experts and teams in achieving superior results. In each case, the primary benefit that AI provides is a high level of automated activity that humans cannot replicate on their own – regardless of how large a team they assemble.
Your Most Secure Alternative
By now, it should be clear that while AI’s applications in cybersecurity are critical, they are also complex, highly specialized, and nearly impossible for any organization to leverage on its own. Any organization that struggles to perform the fundamental day-to-day activities of basic cybersecurity will face significant challenges in developing their own AI-driven cybersecurity platform, integrating it with their existing organizational systems, and continuously improving the performance and outcomes delivered by that system.
This results in a bit of a quandary. Clearly, artificial intelligence software has the potential to significantly improve cybersecurity. And it should be used by the best cybersecurity options. However, self-deployment is nearly impossible.
There is only one viable alternative. You must identify the appropriate partner to incorporate AI into your cybersecurity efforts.
We leverage artificial intelligence to deliver Managed Detection and Response (MDR) services. This is in contrast to traditional MSSPs, which provide only security monitoring alerts. MDR detects threats more thoroughly than traditional MSSPs, which rely heavily on rules and signatures. MDR also utilizes AI and machine learning to investigate, auto-contain threats, and orchestrate response in order to provide a faster response.