Tag: Artificial Intelligence

Part 8 in the Series: Unlock Artificial Intelligence Potential – The Power Of Pristine Data

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; navigating the unique data quality challenges in the cybersecurity domain.

In Part 7 we covered some relevant examples where data is used successfully. While the principles of data hygiene and fidelity are universally applicable, the cybersecurity domain presents unique challenges that require specific considerations when preparing data for AI training.

Attacks

One significant challenge is addressing adversarial attacks targeting training data (https://akitra.com/cybersecurity-implications-of-data-poisoning-in-ai-models/). Cybersecurity AI operates in environments where attackers actively try to manipulate training data. This sets it apart from many other AI applications. Some of the forms this can take are:

• Data poisoning: where attackers inject carefully crafted malicious data into training data sets to skew what a given model learns.
• Adversarial attacks: where subtle modifications are made to input data at inference time to fool a model.

Countering these threats requires the implementation of robust data validation and anomaly detection techniques specifically designed to identify and filter out poisoned data (https://www.exabeam.com/explainers/ai-cyber-security/ai-cyber-security-securing-ai-systems-against-cyber-threats/). Practitioners can improve model resilience by using techniques like adversarial training, explicitly training models on examples of adversarial attacks.

Dynamic Data Maintenance

Another unique challenge in cybersecurity is the continuous battle against evolving cyber threats and the need for dynamic data maintenance. The threat landscape is constantly changing, with new attack vectors, malware strains, and social engineering tactics emerging on a regular basis. This necessitates a continuous process of monitoring and retraining AI models with the latest threat intelligence data to ensure they remain effective against these new threats. Training a model with current state data and thinking that is enough is the equivalent of generating hashes for known malware. The practice outlives its usefulness. As such, the “continuous” part of retraining is one to embrace.

Data hygiene and fidelity processes in the cybersecurity domain must also be agile and adaptable to keep pace with these rapid changes. For example, in Retrieval-Augmented Generation (RAG) architectures, it is crucial to address “authorization drift” by continuously updating the vector databases with the most current document permissions to prevent unauthorized access to sensitive information. Maintaining high data fidelity in cybersecurity requires not only preventing errors and biases. It also requires actively defending against malicious manipulation, and continuously updating data to accurately reflect ever-evolving threat landscapes.

Series Conclusion: Data Quality – The Unsung Hero of Robust AI-Powered Cybersecurity

In conclusion, high-quality data drives the success of AI applications in cybersecurity. Data hygiene, ensuring that data is clean, accurate, and consistent, and data fidelity, guaranteeing that data accurately represents its source and retains its essential characteristics, are not merely technical considerations. They are fundamental pillars upon which effective AI-powered cybersecurity defenses are built. The perils of poor data quality, including missed threats, false positives, biased models, and vulnerabilities to adversarial attacks, underscore the critical need for meticulous data preparation. Conversely, success stories in threat detection, vulnerability assessment, and phishing prevention show how high-quality data enables effective AI models.

Cybersecurity faces evolving challenges, including adversaries manipulating data and new threats emerging constantly. Maintaining strong data quality remains absolutely essential. Organizations must invest in strong data hygiene and fidelity processes to support trustworthy AI-powered cybersecurity. In today’s complex threat landscape, this is a strategic imperative—not just a technical need. Cybersecurity professionals must therefore prioritize navigating the unique data quality challenges in the cybersecurity domain. Data quality above all else will positively impact AI initiatives, it is the unsung hero that underpins the promise of a more secure future cyber landscape.

Part 7 in the Series: Unlock Artificial Intelligence Potential – The Power Of Pristine Data

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; sometimes it is done correctly, data-powered AI: proven cybersecurity examples you need to see.

In Part 6 we covered some data hygiene secrets, or best practices. These can prove to be fundamental to the quality of data used for training models. The importance of high-quality data in AI-powered cybersecurity is underscored by numerous real-world examples of systems that have demonstrated remarkable effectiveness.

Some Examples 

Darktrace stands out as a pioneer in using AI for threat detection. To begin with, its system operates by learning the normal behavior patterns of users, devices, and networks within an organization. Once established, it identifies outliers—deviations that may indicate a cyber threat. Moreover, Darktrace analyzes network and user data in real time, helping prevent cyberattacks across multiple industries. For example, it detected and responded to a ransomware attack at a healthcare organization before the attacker could encrypt critical data. Ultimately, this success hinges on its ability to learn a highly accurate baseline of normal behavior. To achieve this, it requires a continuous stream of clean and representative data.

Constella Intelligence has also demonstrated the power of high-quality data in AI-driven cybersecurity. At the core of their approach, Constella’s solutions focus on identity risk management and threat intelligence, leveraging a vast data lake of curated and verified compromised identity assets. In a notable example, a top global bank used Constella to identify a threat actor and uncover a broader group selling stolen credentials. As a result, Constella’s AI helped stop fraud, saving the bank over $100 million by preventing massive credit card abuse. In addition, Constella’s “Hunter” platform—built on this rich data foundation—has been successfully used by cybercrime investigative journalist Brian Krebs to track and identify key figures in the cybercriminal underworld. Collectively, these examples highlight how Constella’s commitment to data quality empowers their AI-powered solutions to deliver significant cybersecurity impact.

Google’s Gmail has achieved significant success in the realm of phishing detection by leveraging machine learning to scan billions of emails daily. This software identifies and blocks phishing attempts with a high degree of precision. The system learns from each detected phishing attempt, continuously enhancing its ability to recognize new and evolving phishing techniques. This massive scale of operation and the high accuracy rates demonstrate the power of AI when trained on large volumes of well-labeled, clean, diverse email data.

CrowdStrike and SentinelOne show how AI-enhanced EDR can improve threat detection and response on endpoint devices. (https://www.sentinelone.com/cybersecurity-101/data-and-ai/ai-threat-detection/). AI monitors devices for anomalies and responds in real time to detect, contain, or neutralize potential threats. The effectiveness of these platforms relies on their ability to analyze vast amounts of endpoint data to establish baselines of normal activity and to quickly identify and react to deviations that signify anomalous activity.

Getting Predictive

AI algorithms now play a growing role in analyzing extensive repositories of historical security incident data. These repositories typically include records of past breaches, detailed indicators of compromise (IOCs), and intelligence on known threat actors. By mining this historical information, AI can uncover hidden trends and recurring patterns that manual analysis might easily miss. Provided the data is high quality, machine learning models can then use these patterns to predict the likelihood of specific cyberattacks occurring in the future (https://www.datamation.com/security/ai-in-cybersecurity/). As a result, predictive analytics empowers organizations to adopt a more proactive security posture—strategically reinforcing defenses and allocating resources toward the most probable targets. In essence, predictive analytics stands as a cornerstone capability of AI in cybersecurity, enabling threat anticipation and smarter security prioritization.

Consider an organization that utilizes AI to analyze its comprehensive historical security incident data. AI detects recurring phishing attacks targeting finance and HR before fiscal year-end, revealing a seasonal pattern of threats. The organization uses AI predictions to launch tailored security training for finance and HR ahead of high-risk periods. Training helps employees recognize known phishing tactics and avoid similar attacks during future high-risk periods. AI tracks how attacker techniques evolve over time, going beyond just predicting attack type and timing. Organizations can adapt defenses in advance, using AI insights to counter evolving attacker techniques and future threats. Mastercard, for instance, uses AI for predictive analytics to analyze real-time transactions and block fraudulent activities. IBM’s Watson for Cyber Security analyzes historical data to predict future threats.

Detecting Insider Threats and Account Compromises

Organizations increasingly employ AI-powered User and Entity Behavior Analytics (UEBA) tools to analyze vast amounts of user activity data. These include login attempts, file access patterns, network traffic generated by specific users, and their usage of various applications (https://www.ibm.com/think/topics/user-behavior-analytics). The primary goal of this analysis is to establish robust baselines of what constitutes “normal” behavior. This applies to both individual users within the organization and for defined peer groups based on their roles and responsibilities. ML algorithms are then applied to continuously monitor ongoing user activity. The goal is to detect any significant deviations from those established baselines. SThe system flags such deviations as potential signs of compromised accounts, malicious insiders, or other suspicious behavior.

Anomalies may appear when users log in at unusual times or unexpected locations, access sensitive systems outside their usual scope, transfer unusually large volumes of data, or suddenly shift their typical activity patterns. UEBA systems use AI-driven risk scores to rank threats, helping security teams prioritize the most suspicious users and activities. In some cases external sources of identity risk intelligence are factored in as well (https://andresandreu.tech/disinformation-security-identity-risk-intelligence/). UEBA solutions use AI/ML to track user behavior and detect deviations. They also transform raw data into actionable insights by baselining normal behavior and detecting anomalies from there.

Consider an employee who consistently logs into an organization’s network from their office location during standard business hours. An AI-powered UEBA system has this user flagged as risky. This is based on an identity risk posture score that shows evidence of infostealer infections. The UEBA system continuously monitors relevant login activity. It detects a sudden login attempt originating from an IP address in a foreign country at 03:00, a time when the employee is not typically working. This unusual login is immediately followed by a series of access requests to sensitive files and directories. The employee in question does not normally interact with these files as part of their job responsibilities.

The AI system, which already has the user account flagged as risky, recognizes this sequence of events as a significant deviation from the employee’s established baseline behavior. In turn, it flags the activity as a high-risk anomaly. This strongly indicates a potential account compromise and promptly generates an alert for the security team to initiate an immediate action. Beyond detecting overt signs of compromise, AI in UEBA can also identify more subtle indicators of insider threats. For example, attackers may exfiltrate data slowly over time—a tactic that traditional security tools can easily overlook.

AI-driven UEBA needs clean, consistent data from logs, apps, and network activity to build accurate behavioral baselines. Poor data—like missing logs or bad timestamps—can cause false alerts or let real threats go undetected. AI must learn user-specific behavior and adapt to legitimate changes like travel or role shifts to reduce false alarms. Organizations must protect user data and comply with regulations when using systems that monitor and analyze behavior. Finally, it is important to be aware of potential biases that might exist within the user data itself. Biases may cause AI to unfairly flag certain users or behaviors as suspicious, even when they’re actually legitimate.

Part 8 will conclude this series and cover some unique data quality challenges in the cybersecurity domain. Data quality is the foundation for data-powered AI: proven cybersecurity examples you need to see.

Part 6 in the Series: Unlock Artificial Intelligence Potential – The Power Of Pristine Data

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; unlock superior cybersecurity AI with these data hygiene secrets.

In Part 5 we covered some ways how data quality issues can manifest in AI models. Ensuring high-quality data for training AI models in cybersecurity requires a comprehensive and continuous effort. These efforts include effective data cleansing, robust validation processes, and strategic data augmentation techniques.

Data Cleansing

Effective data cleansing is the first critical step. This involves establishing clear data collection processes with stringent guidelines to ensure accuracy and consistency from the onset (https://versium.com/blog/ais-achilles-heel-the-consequence-of-bad-data). Conduct continuous data audits to proactively identify any anomalies, errors, or missing information within datasets. It is essential to remove duplicate records to prevent the skewing of results. It is just as important to handle missing values using appropriate methods such as imputation or removal. Carefully consider the context and potential biases introduced by each approach (https://deepsync.com/data-hygiene/).

Outliers can distort analysis. Manage them using techniques like normalization or Winsorization (https://en.wikipedia.org/wiki/Winsorizing). Maintaining overall consistency is paramount. Require the standardization of data formats and the conversion of data types/encodings to ensure uniformity across all sources. Keeping data in a unified form can help prevent inconsistencies that arise from disparate systems.

Unnecessary or irrelevant data should be eliminated to avoid clutter and improve processing efficiency. Errors need to be actively identified and corrected, and the accuracy of the data should be continuously validated. Leveraging automation and specialized data integration software can significantly streamline these types of data cleansing processes. It is also crucial to maintain proper logs of cleansing activities. Develop proper processes and comprehensive documentation for all data cleaning procedures, maintaining a detailed record of every step taken to ensure transparency and reproducibility. Constant validation throughout the process is key to ensuring the accuracy and suitability of the data for AI training.

Data Validation

Robust data validation is important to ensure the integrity of the data used to train cybersecurity AI models. This involves implementing validation rules that check for data integrity and adherence to predefined criteria, such as encodings, format constraints, and acceptable ranges (https://www.smartbugmedia.com/blog/data-hygiene-best-practices-tips-for-a-successful-integration). Automated validation checks can be employed through rule-based validation, where specific criteria are defined, and machine learning-based validation, where algorithms learn patterns of valid data. Utilizing specialized data quality tools can further enhance this process.

Specific validation techniques include:

• Performing data range validation to ensure values fall within expected limits.
• Data format validation to check the structure of the data.
• Data type validation to confirm that data is in the correct format (e.g., numeric, text, date).

Conducting uniqueness checks to identify duplicate entries and implementing business rule validation to ensure data meets specific organizational requirements are also critical. Ensuring data completeness through continuous systematic checks is another vital aspect of validation. While automation plays a significant role, teams should also conduct manual reviews and spot checks to verify the accuracy of data handled by any automated processes. Establishing a comprehensive data validation framework and finding the right balance between the speed and accuracy of validation are key to ensuring the quality of the training data.

Data Augmentation

Data augmentation is a powerful optional technique to further enhance the quality and robustness of cybersecurity AI models (https://www.ccslearningacademy.com/what-is-data-augmentation/). This involves synthetically increasing the size and diversity of a training dataset by creating modified versions of existing data. Data augmentation can help prevent overfitting by exposing a model to a wider range of scenarios and variations. This can lead to improved accuracy and the creation of more robust and adaptive protective mechanisms.

Various techniques can be used for data augmentation, including:

• Text based (e.g. word / sentence shuffling, random insert / delete actions)
• Image based (e.g. adjusting brightness / contrast, rotations)
• Audio based (e.g. noise injection, speed / pitch modifications)
• Generative adversarial networks (GANs)

The generative techniques are interesting because they can generate examples of edge cases or novel attack scenarios to improve anomaly detection capabilities. Furthermore, teams can strategically employ data augmentation to address the underrepresentation of certain concepts or to mitigate bias in training data.

Ultimately, a comprehensive strategy combines rigorous data cleaning, thorough validation, and thoughtful data augmentation. Unlock superior cybersecurity with these data hygiene secrets in order to build high-quality datasets required to train effective and reliable AI models. Some of these techniques have been employed by the examples covered in Part 7 – Success Stories: Real-World Examples of Effective Cybersecurity AI Driven by High-Quality Data.

Part 5 in the Series: Unlock Artificial Intelligence Potential – The Power Of Pristine Data

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; technical insights: how data quality issues manifest in AI models.

In Part 4 we covered the data fidelity crisis and some of the dynamics that can create it. Additionally, the consequences of poor data quality extend to the technical performance of AI models, manifesting in several critical ways that can directly impact the effectiveness of cybersecurity defenses.

One common manifestation is the increased rates of false positives and negatives (https://www.drugtargetreview.com/article/152326/part-two-the-impact-of-poor-data-quality/). Noise, inconsistencies, and biases within training data can confuse an AI model. This confused state makes it difficult for an AI engine to accurately distinguish between legitimate and malicious activities. High rates of false positives, where benign events are incorrectly flagged as threats, can overwhelm security teams. This barrage of white noise and alerts can lead to alert fatigue and potentially cause teams to overlook genuine threats (https://www.researchgate.net/publication/387326774_Effect_of_AI_Algorithm_Bias_on_the_Accuracy_of_Cybersecurity_Threat_Detection_AUTHORS). Conversely, high rates of false negatives, where actual attacks go undetected, can leave environments vulnerable and exposed to significant damage.

Another technical issue arising from poor data quality is that of overfitting to noisy data (https://dagshub.com/blog/mastering-duplicate-data-management-in-machine-learning-for-optimal-model-performance/). When AI models are trained on datasets containing a significant amount of irrelevant or misleading data, they can learn to fit the training data too closely, including the noise itself. This results in models that perform very well on the training data. But they fail to generalize effectively to new, unseen data. In the dynamic landscape of cybersecurity, where new threats and attack techniques are constantly emerging, the ability of an AI model to generalize is crucial for its long-term effectiveness.

Indeed, AI models often learn and amplify biases from low-fidelity data. These biases can lead to skewed predictions and discriminatory outcomes. Moreover, attackers who understand the biases inherent in a particular AI model can potentially exploit these weaknesses to their advantage. For example, consider an AI-powered Intrusion Detection System (IDS) primarily trained on network traffic data from large enterprise environments. It might struggle to accurately identify atypical network traffic patterns in smaller environments. This could create a security gap for an organization. Or consider applying that same IDS to a manufacturing network. Here the communication protocols are radically different from the original training source environment. You will not achieve the expected outcome. Data quality issues, therefore, not only affect the overall accuracy of AI models but can also lead to specific, exploitable scenarios that malicious actors can potentially leverage.

Here is a table summarizing some of what was covered in Part 5 of this series:

Data Quality Issue	Technical Manifestation	Implications for Cybersecurity
Noise	Increased false positives and negatives	Alert fatigue, missed threats
Incompleteness	Missed threats (false negatives)	Vulnerabilities remain undetected
Inconsistency	False positives and negatives	Difficulty in identifying true patterns
Bias	Skewed predictions	Discriminatory outcomes, exploitable weaknesses
Manipulation	Incorrect classifications	Compromised security posture
Outdated Data	Failure to detect new threats	Decisions based on irrelevant information, increased false negatives

Part 6 will cover best practices for cultivating data hygiene and fidelity in cybersecurity AI training. This next session is critical as a follow up to technical insights: how data quality issues manifest in AI models.

Part 4 in the Series: Unlock Artificial Intelligence Potential – The Power Of Pristine Data

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; to avoid a data fidelity crisis: secure AI now before cybersecurity fails.

In Part 3 we covered some examples of cybersecurity AI applications and how they could be negatively impacted. Beyond the general cleanliness of data, the fidelity, or the accuracy and faithfulness of the data to its source, plays a crucial role in ensuring the trustworthiness of AI applications in cybersecurity.

Inaccurate Data

The use of inaccurate data in training AI models can have profound negative consequences. It can lead to flawed outcomes, resulting in significant repercussions for organizations relying on these systems for security. For instance, take an active protection system designed for an Industrial Control Systems (ICS) environment. Protection can be based on set point values that mean physical changes to some equipment. These set point values typically exist in some range for normal operational functions. A model can be trained with inaccurate data values that are outside the range of normal operational parameters. This means bad data may get past the active protection system. This in turn could have a potential physical impact.

Biased Data

Inaccurate or unreliable AI results can erode user trust and confidence in an entire AI system. Users can become hesitant to rely on its outputs for critical security decisions. Biased training data is another significant concern that can compromise the fidelity of AI models. AI models learn from the patterns present in their training data. If this data reflects existing societal, historical, or systemic biases, the AI model will likely inherit and operationalize those biases (https://versium.com/blog/ais-achilles-heel-the-consequence-of-bad-data). In cybersecurity, this can lead to the development of unfair or ineffective security measures. These can take the form of AI systems that disproportionately flag activities from certain user groups or source countries as suspicious.

Biased data can also result in AI models that perform poorly. This can manifest as an increased rate of false positives or false negatives for specific demographics. In turn, this can skew the overall fairness and effectiveness of a security system (https://interface.media/blog/2024/12/24/exploring-the-impact-of-ai-bias-on-cybersecurity/).

Poisoned Data

One of the most concerning threats to data fidelity in AI is the risk of manipulated or poisoned data. Data poisoning is when malicious actors intentionally introduce false or misleading data into some training process. This is done to either degrade the AI model’s performance or to cause it to behave in a way that benefits the attacker (https://akitra.com/cybersecurity-implications-of-data-poisoning-in-ai-models/). These types of attacks can be very difficult to detect. Especially if there is a lack of intimacy with the original unpoisoned data set. These types of attacks can lead to compromised security postures where AI models put cybersecurity resources into time suck scenarios, fail to detect real threats, or flag legitimate actions as suspicious. Model poisoning can also result in biased outcomes, provide unauthorized access to systems, or cause a disruption of critical services.

A related threat is that of adversarial attacks (e.g. Adversarial AI). This is where subtle modifications are made to the input data at the time of inference to intentionally fool an AI model into making incorrect classifications or decisions. In the context of cybersecurity, this could involve attackers subtly altering malware signatures to evade detection by AI-powered antivirus systems. Another example is the alteration of AI managed Web Application Firewall (WAF) rulesets and/or regular expressions.

The integrity of training data is therefore paramount. Biases can lead to systemic flaws in how security is applied. Intentional manipulation can directly undermine the AI’s ability to function correctly. This creates potential new attack surface elements where none previously existed.

Part 5 will cover some technical insights to unlock artificial intelligence potential and avoid a data fidelity crisis: secure AI now before cybersecurity fails.

Part 3 in the series -Unlock Artificial Intelligence Potential – The Power Of Pristine Data

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; bad data is undermining your cybersecurity AI right now.

In Part 2 we covered the perils of poor data hygiene. The promise of AI in cybersecurity lies in its ability to outperform a group of qualified humans. From the defenders perspective, this generally equates to:

• analyzing vast amounts of data
• identifying subtle patterns
• responding to threats at speeds that are impossible for those human analysts

Like so many things in AI, the effectiveness of these applications heavily depends on the quality of the data used to train them. Poor data hygiene can severely cripple the performance of AI in critical cybersecurity tasks.

Threat Detection

Once everyone gets past the hype, threat Detection is a major area where cybersecurity practitioners are expecting much benefit. AI has shown immense potential, excelling at identifying patterns and anomalies in real-time to flag potential security threats. However, the presence of poor data hygiene can significantly undermine this capability.

Missed threats, also known as false negatives, represent a major area of impact. The formula here is relatively straightforward: AI models require data to understand what constitutes a threat. If that training data is incomplete or if it lacks examples of new and evolving attack patterns, the AI might fail to recognize novel threats when they first appear in the real world. Biased data can also lead to AI engines overlooking certain attacks, potentially creating blind spots in a security ecosystem (https://www.researchgate.net/publication/387326774_Effect_of_AI_Algorithm_Bias_on_the_Accuracy_of_Cybersecurity_Threat_Detection_AUTHORS).

On the other end of the spectrum are false positives. Poor data hygiene can lead to these phenomena where AI incorrectly flags benign activities as malicious. This can be caused by noisy or inconsistent data that confuses a model, leading it to misinterpret normal behavior as suspicious. The consequence of excessive false positives is often white noise and alert fatigue among security teams. The constant stream of non-genuine alerts can cause analysts to become desensitized. The risk is then potentially missing actual threats that in some cases are blatantly obvious.

Bias in the training data can also result in a reduced ability to detect novel threats. This can lead to inaccurate assessments, causing a misprioritization of security efforts. The effectiveness of AI in threat detection fundamentally depends on the diversity and representativeness of the training data. If the data does not cover the full spectrum of attack types and normal network behaviors, the AI will struggle to accurately distinguish between them.

Vulnerability Assessment

Another critical cybersecurity function increasingly employing AI is vulnerability assessment, where AI continuously scans systems (networks, applications, APIs, etc.) for weaknesses and prioritizes them based on potential impact. Organizations highly value this capability because human resources cannot keep pace with the volume of findings in larger environments. Business context plays a huge role here. It becomes the driver for what is a priority to a given organization. Business context would therefore be a data set used to train models for the purpose of vulnerability assessments.

Inaccurate data can severely hinder the reliability of AI in this area. Inaccurate data can severely hinder the reliability of AI in this area. Incomplete or incorrect training data, or mislabeling assets, may cause AI to miss or misprioritize vulnerabilities. This could leave systems exposed to potential exploitation. Conversely, inaccurate data could also lead to the AI incorrectly flagging non-existent vulnerabilities or treating assets of non-value as critical, wasting resources on addressing threats incorrectly.

Biased or outdated data can also result in an inaccurate prioritization of vulnerabilities. This can lead to a misallocation of security resources towards less critical issues while more severe weaknesses remain unaddressed. Ultimately, poor data hygiene can lead to a compromised security posture due to unreliable assessments of the true risks faced by an organization. AI’s ability to effectively assess vulnerabilities depends on having precise and current information about the business and vulnerabilities themselves. Inaccuracies in this foundational data can lead to a false sense of security or the inefficient deployment of resources to address vulnerabilities that pose minimal actual risk.

Phishing Detection

The detection of phishing activity has seen significant advancements through the application of AI. This is particularly so with the use of Natural Language Processing (NLP). NLP allows AI to analyze email content, discover sentiment, identify sender behavior, and use contextual information to identify and flag potentially malicious messages. Despite its successes, the effectiveness of AI in phishing detection is highly sensitive to poor data hygiene.

One significant challenge is the failure to detect sophisticated attacks. If the training data used to teach the AI what constitutes a phishing email lacks examples of the latest and most advanced phishing techniques the AI might not be able to recognize these new threats. The scenario of AI vs AI is becoming a reality in the realm of phishing detection. The defensive side is up against those leveraging generative AI to create highly realistic, strategic, and personalized messages. This is particularly concerning as phishing tactics are becoming very realistic and they are constantly evolving to evade detection.

Inconsistent or noisy data within email content or sender information can lead to an increase in false positives. Legitimate emails could get incorrectly flagged as phishing attempts. This can disrupt communication and lead to user frustration. Bias in training data can cause AI to miss phishing attacks targeting certain demographics or generate excessive false positives. Given the ever-changing nature of phishing attacks, it is crucial for AI models to be continuously trained on diverse and up-to-date datasets that include examples of the most recent and sophisticated tactics employed by cybercriminals. Poor data hygiene can leave the AI unprepared and ineffective against these evolving threats.

Part 4 will cover the significance of data fidelity and how the lack of trustworthiness can negatively impact an environment. Bad data is undermining your cybersecurity AI right now.

Part 2 in the Series: Unlock Artificial Intelligence Potential – The Power Of Pristine Data

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; there could be a high price to pay for the perils of poor data hygiene: undermining AI training.

In Part 1 we covered the power of pristine data. Neglecting data hygiene can have severe consequences for those who depend on AI for information. Data hygiene is directly correlated to the training and performance of AI models, particularly in the critical domain of cybersecurity. Several common data quality issues can significantly undermine the effectiveness of even the most sophisticated AI algorithms.

Missing Data

One prevalent issue is incomplete data sets, in particular the presence of missing values in datasets (https://www.geeksforgeeks.org/ml-handling-missing-values/). This is a common occurrence in real-world data collections due to various factors such as technical debt, software bugs, human errors, or privacy concerns. The absence of data points for certain variables can significantly harm the accuracy and reliability of AI models. The lack of complete information can also reduce the effective sample size available for training and tuning. This potentially leads to a decrease in a model’s ability to generalize. Furthermore and slightly more complicated, if the reasons behind missing data points are not random, the introduction of bias into some models becomes a real-world concern. In this scenario a model might learn skewed relationships based on the incomplete data set. Ultimately, mishandling missing values can lead to biased and unreliable results, significantly hindering the overall performance of AI models.

Incomplete data can prevent ML models from identifying crucial patterns or relationships that exist within the full dataset. Addressing missing values typically involves either:

• Removing data: deleting the rows or columns containing the missing elements. This comes with the risk of reducing a dataset and potentially introducing biased results if the reason for the data to be missing is not based on randomness.
• Imputation techniques: employing imputation techniques to fill in the missing values with guessed data. While this preserves the dataset size it can introduce its own form of bias if the guesses are inaccurate. 

The fact that missing data can systematically skew a model’s learning process, leading to inaccurate and potentially biased outcomes, highlights the importance of understanding the nature of the missingness. The type of missingness are:

• Missing Completely At Random (MCAR)
• Missing At Random (MAR)
• Missing Not At Random (MNAR)

Understanding the reason at hand directly impacts the strategies for addressing this issue. Arbitrarily filling in missing values without understanding the underlying reasons can be more detrimental than beneficial.

Duplicate Data

Moving beyond missing data elements, another significant challenge is that of duplicate data within training datasets. While the collection of massive datasets has become easier, the presence of duplicate records can considerably impact quality and ultimately the performance and accuracy of AI models trained on this data. This can obviously lead to biased outcomes. Duplicate entries can negatively affect model evaluation by creating a biased evaluation. This occurs primarily when exact or near-duplicate data exists in both training and validation sets, leading to an overestimation of a model’s performance on unknown data. Conversely, if a model performs poorly on the duplicated data point, it can artificially deflate the overall performance metrics. Furthermore, duplicate data can lead to overfitting. This is where a model becomes overly specialized and fails to capture underlying patterns on new unseen data sets. This is particularly true with exact or near duplicates, which can reinforce patterns that may not be real when considering a broader data set.

The presence of duplicate data is also computationally expensive. It increases training costs with necessary computational overhead for preprocessing and training. Additionally, duplicate data can lead to biased feature importance, artificially skewing the importance assigned to certain features if they are consistently associated with duplicated instances. In essence, duplicate entries can distort the underlying distribution of a larger data set. This lowers the accuracy of probabilistic models. It is worth noting that the impact of duplicate data isn’t always negative and can be context-dependent. In some specific scenarios, especially with unstructured data, duplicates might indicate underlying issues with data processing pipelines (https://indicodata.ai/blog/should-we-remove-duplicates-ask-slater/). For Large Language Models (LLMs) the repetition of high-quality examples might appear as near-duplicates. This can sometimes aid in the registering of important patterns (https://dagshub.com/blog/mastering-duplicate-data-management-in-machine-learning-for-optimal-model-performance/). This nuanced view suggests that intimate knowledge of a given data set, and the goals of an AI model, are necessary when strategizing on how to handle duplicate data.

Inconsistent Data

Inconsistent data, or a data set characterized by errors, inaccuracies, or irrelevant information, poses a significant threat to the reliability of AI models. Even the most advanced and sophisticated models will yield unsatisfactory results if trained on data of poor quality. But, inconsistent data can lead to inaccurate predictions, resulting in flawed decision-making with contextually significant repercussions. For example, an AI model used for deciding if an email is dangerous might incorrectly assess risk, leading to business impacting results. Similarly, in security operations, a log analyzing AI system trained on erroneous data could incorrectly classify nefarious activity as benign.

Incomplete or skewed data can introduce bias if the training data does not adequately represent the diversity of the real-world population. This can perpetuate existing biases, affecting fairness and inclusivity. Dealing with inconsistent data often necessitates significant time and resources for data cleansing. This leads to operational inefficiencies and delays in project timelines. Inconsistent data can arise from various sources, including encoding issues, human error during processing, unhandled software exceptions, variations in how data is recorded across different systems, and a general lack of standardization. Addressing this issue requires establishing uniform data standards and robust data governance policies throughout an organization to ensure that data is collected, formatted, and stored consistently. The notion of GIGO accurately describes the direct relationship between the quality of input data and the reliability of the output produced by AI engines.

Here is a table summarizing some of what was covered in Part 2 of this series:

Data Quality Issue	Impact on Model Training	Potential Consequences
Missing Values	Reduced sample size, introduced bias, analysis limitations	Biased and unreliable results, missed patterns
Duplicate Data	Biased evaluation, overfitting, increased costs, biased feature importance	Inflated accuracy, poor generalization
Inconsistent Data	Unreliable outputs, skewed predictions, operational inefficiencies, regulatory risks	Inaccurate decisions, biased models

Part 3 will cover cybersecurity applications and how bad data impacts the ability to unlock artificial intelligence potential – strengthening the notion of the perils of poor data hygiene: undermining AI training.

The integration of Artificial Intelligence (AI) into cybersecurity has ushered in a new era of sophisticated threat detection, proactive vulnerability assessments, and automated incident response. As organizations increasingly rely on AI to bolster their defenses, the fundamental principle remains that the quality of the data on which they train these advanced systems directly links to their effectiveness. The old saying “garbage in, garbage out” (GIGO) holds true here; to unlock artificial intelligence potential – the power of pristine data is of the utmost importance.

Part 1 – Defining Data Hygiene and Fidelity in the Context of AI and Machine Learning

Outside of the realm of areas like unsupervised learning, the foundation of any successful AI application lies in the data that fuels its models and learning processes. In cybersecurity, the stakes are exceptionally high. Consider a small security operations team that has a disproportionate scope of responsibility. Rightfully so, this team may rely on a Generative Pre-training Transformer (GPT) experience to balance out the team size against the scope of responsibility. If that GPT back-end data source is not solid this team could suffer due to inaccuracies and time sucks that lead to suboptimal results. The need for clean data is paramount. This goal encompasses two key concepts:

• Data Hygiene
• Data Fidelity

Data Hygiene

Data Hygiene refers to processes required to ensure that data is “clean”. Meaning it is free from errors, inaccuracies, and inconsistencies (https://www.telusdigital.com/glossary/data-hygiene). Several essential aspects contribute to good data hygiene:

• Accuracy: This is fundamental, ensuring that the information is correct and devoid of mistakes such as misspellings or incorrect entries. More importantly, accuracy will have a direct impact in not introducing bias into any learning models.
• Completeness:  This is equally vital to accuracy in terms of what feeds a given model. Requiring datasets that contain all the necessary information, and avoid missing values that could skew results, is a must.
• Consistency: Consistency ensures uniform data formatting and standardizes entries across different datasets, preventing contradictions. This can have a direct impact on the effectiveness of back-end queries. For example, internationally date formats vary. To create an effective time range query, format those stored values consistently.
• Timeliness: This dictates that the data must be current and relevant for the specific purpose of training an AI model. This doesn’t exclusively mean current based on the data timestamp, legacy data needs to also be available in a timely fashion.
• De-duplication: The data removal process is crucial to maintain accuracy, avoid redundancy, and minimize any potential bias in the model training process.

Implementing a robust data hygiene strategy for an AI project yields numerous benefits, including improved accuracy of AI models, reduced bias, and ultimately saves time and financial resources that organizations would otherwise spend correcting unsatisfactory results (https://versium.com/blog/ais-achilles-heel-the-consequence-of-bad-data). Very much like cybersecurity endeavors themselves, data hygiene cannot be an afterthought. The consistent emphasis on these core hygiene attributes highlights their fundamental importance for any data-driven application. Especially in the critical field of AI. Moreover, maintaining data hygiene is not a one-time effort. It is a continuous set of processes and a commitment that involves regular audits and possible rebuilds of data systems, standardization of data input fields, automation of cleansing processes to detect anomalies and duplicates, and continuous processes to prevent deterioration of quality. This continuous maintenance is essential in dynamic environments such as cybersecurity, where data can quickly become outdated or irrelevant.

Data Fidelity

Data Fidelity focuses on integrity, accurately representing data from its original source while retaining its original meaning and necessary detail (https://www.qualityze.com/blogs/data-fidelity-and-quality-management). It is driven by several key attributes:

• Accuracy: In the context of data fidelity, accuracy means reflecting the true characteristics of the data source without distortion. The data has a high level of integrity and has not been tampered with.
• Granularity: This refers to maintaining the required level of detail in the data. This is particularly important in cybersecurity where subtle nuances in event logs or network traffic can be critical. A perfect example in the HTTP realm is knowing that a particular POST had a malicious payload but not seeing the payload itself.
• Traceability: This is another important aspect, allowing for the tracking of data back to its origin. This can prove vital for understanding the context and reliability of the information as well as providing reliable signals for a forensics exercise.

Synthetic data is a reality at this point. It is increasingly used to populate parts of model training datasets. Due to this, statistical similarity to the original, real-world data is a key measure of fidelity. High data fidelity is crucial for AI and Machine Learning (ML). It ensures models learn from data that accurately mirrors the real-world situations they aim to analyze and predict.

This is particularly critical in sensitive fields like cybersecurity, where even minor deviations from the true characteristics of data could lead to flawed security assessments or missed threats (https://www.qualityze.com/blogs/data-fidelity-and-quality-management). The concept of fidelity extends beyond basic accuracy to include the level of detail and the preservation of statistical properties. This becomes especially relevant when dealing with synthetically generated data or when aiming for explainable AI models. 

The specific interpretation of “fidelity” can vary depending on the particular AI application. For instance, in intrusion detection, it might refer to the granularity of some data captured from a specific event. Yet in synthetic data generation, “fidelity” emphasizes the statistical resemblance to some original data set. In explainable AI (XAI), “fidelity” pertains to the correctness of the explanations provided by a model (https://arxiv.org/html/2401.10640v1). While accuracy remains a core component, the precise definition and emphasis of fidelity are context-dependent, reflecting diverse ways in which AI can be applied to the same field.

Here is a table summarizing some of what was covered in Part 1 of this series:

Concept	Definition	Key Attributes	Importance for AI/ML
Data Hygiene	Process of ensuring data is clean	Accuracy, Completeness, Consistency, Timeliness, De-duplication	Improves accuracy, reduces bias, better performance
Data Fidelity	Accuracy of data representing its source	Accuracy, Granularity, Traceability, Statistical Similarity	Ensures models learn from accurate and detailed data, especially nuanced data

Part 2 will cover the perils of poor data hygiene and how this negatively impacts the ability to unlock artificial intelligence potential – the power of pristine data.