AI Cybersecurity: Everything You Need To Know

The world of cybersecurity is constantly changing, and it requires new and creative ways to deal with the ever-growing complexity of cyber threats. AI has become an important cybersecurity weapon, providing both exciting advantages and some tricky obstacles.

Today’s security teams face many challenges—sophisticated cyber attackers, an expanding attack surface, an explosion of data, and growing infrastructure complexity—that hinder their ability to safeguard data, manage user access, and quickly detect and respond to security threats. AI cybersecurity provides transformative solutions that optimize analysts’ time—by accelerating threat detection, expediting responses, and protecting user identity and datasets—while keeping cybersecurity teams in the loop and in charge.

What is AI?

AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks and make decisions that typically require human intelligence. It involves creating algorithms and models that enable machines to learn from data, recognize patterns, and adapt to new information or situations.

In simple terms, AI is like teaching computers to think and learn like humans. It allows machines to process and analyze large amounts of data, identify patterns or anomalies, and make predictions or decisions based on that information. AI can be used in various applications, such as image and speech recognition, natural language processing, robotics, and cybersecurity, to name a few.

Overall, AI aims to mimic human intelligence to solve complex problems, automate tasks, and enhance efficiency and accuracy in different fields.

Machine learning and deep learning

Machine learning (ML) is a commonly used subset of AI. ML algorithms and techniques allow systems to learn from data and make decisions without being explicitly programmed.

Deep learning (DL) is a subset of ML that leverages artificial computational models inspired by the human brain called neural networks for more advanced tasks. ChatGPT is an example of AI that uses ML to understand and respond to human-generated prompts.

Narrow AI and artificial general intelligence

All types of AI are considered Narrow AI. Their scope is limited, and they’re not sentient. Examples of such AI are voice assistants, chatbots, image recognition systems, self-driving vehicles, and maintenance models.

Artificial general intelligence (AGI) is a hypothetical concept that refers to a self-aware AI that can match or even surpass human intelligence. While some experts estimate that AGI is several years or even decades away, others believe that it’s impossible.

Generative AI

Generative AI refers to a subset of artificial intelligence techniques that involve the creation and generation of new content, such as images, text, audio, or even videos. It involves training models to understand patterns in existing data and then using that knowledge to generate new, original content that resembles the training data.

One popular approach to generative AI is the use of generative adversarial networks (GANs). GANs consist of two neural networks: a generator network and a discriminator network. The generator network creates new content, while the discriminator network evaluates and distinguishes between the generated content and real content. The two networks work competitively, with the generator attempting to produce content that the discriminator cannot distinguish from real data.

Generative AI has applications in various domains. For example:

Image Generation: Generative AI can be used to generate realistic images, such as creating photorealistic faces, landscapes, or even entirely new objects that do not exist in the real world.
Text Generation: Generative models can be trained to generate coherent and contextually relevant text, which can be used for tasks like chatbots, content creation, or language translation.
Music and Audio Generation: Generative AI can create new musical compositions or generate realistic sounds and voices.

AI in cybersecurity

Artificial intelligence and cybersecurity have been touted as revolutionary and much closer than we might think. However, this is only a partial truth that must be approached with reserved expectations. The reality is that we may be faced with relatively gradual improvements for the future to come. In perspective, what may seem gradual when compared to a fully autonomous future is actually still leaps beyond what we’ve been capable of in the past.

As we explore the possible implications of security in machine learning and AI, it’s important to frame the current pain points in cybersecurity. There are many processes and aspects we’ve long accepted as normal that can be treated under the umbrella of AI technologies.

New threat identification and prediction

New threat identification and prediction is a factor that impacts response timeframes for cyber attacks. As noted previously, lag time already occurs with existing threats. Unknown attack types, behaviors, and tools can further deceive a team into slow reactions. Worse, quieter threats like data theft can sometimes go completely undiscovered.

An April 2020 survey by Fugue gathered that roughly 84% of IT teams were concerned over their cloud-based systems being hacked without their awareness.

Constant attack evolution leading to zero-day exploits is always an underlying concern within network defense efforts. But for some good news, cyber-attacks are not commonly built from scratch. Being that they are often constructed atop behaviors, frameworks, and source codes of past attacks, machine learning has a pre-existing path to work from.

Programming based on ML can help highlight commonalities between the new threat and previously identified ones to help spot an attack. This is something that humans cannot effectively do in a timely fashion and further highlights that adaptive security models are necessary.

From this viewpoint, machine learning can potentially make it easier for teams to predict new threats and reduce lag time due to increased threat awareness.

Human error in configuration

Human error is a significant part of cybersecurity weaknesses. For example, the proper system configuration can be incredibly difficult to manage, even with large IT teams engaging in setup. In the course of constant innovation, computer security has become more layered than ever. Responsive tools could help teams find and mitigate issues that appear as network systems are replaced, modified, and updated.

Consider how newer internet infrastructure like cloud computing may be stacked atop older local frameworks. In enterprise systems, an IT team will need to ensure compatibility to secure these systems. Manual processes for assessing configuration security cause teams to feel fatigued as they balance endless updates with normal daily support tasks.

With smart, adaptive automation, teams could receive timely advice on newly discovered issues. They could get advice on options for proceeding or even have systems in place to automatically adjust settings as needed.

Threat alert fatigue

Threat alert fatigue gives organizations another weakness if not handled with care. Attack surfaces are increasing as the aforementioned layers of security become more elaborate and sprawling. Many security systems are tuned to react to many known issues with a barrage of purely reflexive alerts. As a result, these individual prompts leave human teams to parse out potential decisions and take action.

A high influx of alerts makes this level of decision-making an especially taxing process. Ultimately, decision fatigue becomes a daily experience for cybersecurity personnel. Proactive action for these identified threats and vulnerabilities is ideal, but many teams lack the time and staffing to cover all their bases.

Sometimes teams have to decide to confront the largest concerns first and let the secondary objectives fall by the wayside. Using AI within cybersecurity efforts can allow IT teams to manage more of these threats in an effective, practical fashion. Confronting each of these threats can be made much easier if batched by automated labeling. Beyond this, some concerns may be able to be treated by the machine learning algorithm itself.

Human efficiency with repeated activities

Human efficiency is another pain point within the cybersecurity industry. No manual process is perfectly repeatable every time, especially in a dynamic environment such as ours. The individual setup of an organization’s many endpoint machines is among the most time-consuming tasks.

Even after the initial setup, IT teams find themselves revisiting the same machines later on to correct misconfigurations or outdated setups that cannot be patched in remote updates.

Furthermore, when employees are tasked with responses to threats, the scope of said threat can rapidly shift. Where human focus may be slowed by unexpected challenges, a system based on AI and machine learning can move with minimal delay.

Threat response time

Threat response time is absolutely among the most pivotal metrics for a cybersecurity team’s efficacy. From exploitation to deployment, malicious attacks have been known to move very quickly. Threat actors of the past used to have to sift through network permissions and disarm security laterally for sometimes weeks on end before launching their attack.

Unfortunately, experts in the cyber defense space are not the only ones benefiting from technology innovations. Automation has since become more commonplace in cyber attacks. Threats like the recent LockBit ransomware attacks have accelerated attack times considerably. Currently, some attacks can even move as quickly as half an hour.

The human response can lag behind the initial attack, even with known attack types. For this reason, many teams have more often engaged in reactions to successful attacks rather than prevention of attempted attacks. On the other end of the spectrum, undiscovered attacks are a danger all their own.

ML-assisted security can pull data from an attack to be immediately grouped and prepared for analysis. It can provide cybersecurity teams with simplified reports to make processing and decision-making a cleaner job. Going beyond just reporting, this type of security can also offer recommended action for limiting further damage and preventing future attacks.

How enterprises are using AI for cybersecurity

AI-powered reactive solutions have gained significant traction in the enterprise world, enabling organizations to enhance their incident response processes and mitigate the impact of cyberattacks.

Automated remediation

AI can automate the remediation process by initiating predefined actions based on incident categorization and severity. This capability reduces the need for manual intervention and accelerates incident response times. These systems can execute actions such as isolating compromised systems, blocking malicious IP addresses or quarantining infected files.

A healthcare organization may deploy an AI-driven endpoint protection solution to detect a malware infection on a workstation. If it does, it can automatically isolate the affected device from the network, preventing lateral movement and further damage.

Security Information And Event Management (SIEM) Systems

Enterprises employ SIEM systems that use AI algorithms to analyze and categorize security incidents in real-time. These systems collect and correlate data from various sources such as network logs, firewall logs, and intrusion detection systems. By applying machine learning techniques, SIEM systems can identify patterns and anomalies indicative of cyber threats, enabling security teams to respond quickly and effectively.

A financial institution may employ a SIEM system that uses AI to automatically analyze and categorize security events. If the system detects a pattern resembling a distributed denial-of-service (DDoS) attack, it can trigger an automated response to block the malicious traffic, preventing service disruptions.

Threat intelligence platforms

AI-powered threat intelligence platforms aggregate and analyze vast amounts of data from internal and external sources to identify potential threats. Natural language processing and machine learning algorithms are used to analyze unstructured data like social media feeds, dark web forums and security blogs.

Extracting actionable insights from this data allows organizations to proactively identify emerging threats and vulnerabilities.

An e-commerce company may use a threat intelligence platform that leverages AI to monitor social media for discussions related to its brand. If the platform detects a sudden increase in mentions of the company along with keywords and patterns, it can automatically alert the security team so that it can investigate and respond promptly.

Challenges surrounding AI cybersecurity implementation

As enterprises increasingly embrace AI in their cybersecurity strategies, they also encounter various challenges that must be overcome for successful implementation.

Adversarial attacks

Cyberattackers are becoming increasingly sophisticated in their methods, leveraging adversarial attacks to deceive AI systems. Adversarial attacks involve manipulating input data to mislead these algorithms and evade detection. Enterprises must continually invest in research and development to enhance the resilience of their AI models against such attacks.

Moreover, AI-powered systems in cybersecurity are prone to generating false positives—mistakenly identifying harmless activities as malicious threats. False positives can lead to alert fatigue and divert valuable resources toward investigating nonexistent threats, potentially causing disruptions in business operations.

A financial institution deploying an AI-based fraud detection system may face challenges in fine-tuning the model to reduce false positives without compromising its ability to detect genuine fraudulent activities.

Automated malware

An AI like ChatGPT is excellent at accurately crunching numbers. According to Columbia Business School professor Oded Netzer, ChatGPT can already “write code quite well.” Experts say that soon, it may help software developers, computer programmers, and coders or displace more of their work.

While software like ChatGPT has some protections to prevent users from creating malicious code, experts can use clever techniques to bypass it and create malware. For example, one researcher was able to find a loophole and create a nearly undetectable complex data-theft executable. The executable had the sophistication of malware created by a state-sponsored threat actor.

This could be the tip of the iceberg. Future AI-powered tools may allow developers with entry-level programming skills to create automated malware, like an advanced malicious bot. A malicious bot can steal data, infect networks, and attack systems with little to no human intervention.

Ethical & regulatory challenges

The implementation of AI in cybersecurity also raises ethical and regulatory concerns. Enterprises must ensure that their systems adhere to legal requirements and ethical standards, such as privacy regulations and fairness in decision-making.

AI has the potential to revolutionize cybersecurity, but its challenges must be carefully addressed to ensure accurate and beneficial outcomes. Overcoming the challenges can allow organizations to harness the full potential of AI in protecting their digital assets and combating emerging cyber threats.

A healthcare organization employing these algorithms to analyze patient data for anomaly detection must navigate the complexities of data privacy laws and maintain strict patient confidentiality.

Quality of data

AI algorithms rely heavily on large volumes of high-quality data to train and improve their accuracy. However, many enterprises struggle to gather sufficient and relevant data due to factors such as data silos, privacy concerns and regulatory constraints.

A multinational organization may find it challenging to aggregate and normalize security logs from various subsidiaries spread across different regions, each using different systems and formats. Without access to comprehensive and diverse data, the models may not achieve optimal results, leading to less effective threat detection and response.

Data manipulation and data poisoning

While AI is a powerful tool, it can be vulnerable to data manipulation. After all, AI is dependent on its training data. If the data is modified or poisoned, an AI-powered tool can produce unexpected or even malicious outcomes.

In theory, an attacker could poison a training dataset with malicious data to change the model’s results. An attacker could also initiate a more subtle form of manipulation called bias injection. Such attacks can be especially harmful in industries such as healthcare, automotive, and transportation.

AI Cybersecurity: Everything You Need To Know

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