AI is a powerful tool for enhancing cybersecurity, but it also comes with significant challenges. In this article, you will learn about some of the biggest obstacles to using AI in cybersecurity, and how to overcome them.
One of the main requirements for AI to work effectively is high-quality data. However, cybersecurity data is often noisy, incomplete, or outdated, which can affect the accuracy and reliability of AI models. Moreover, data sources may be compromised or manipulated by adversaries, who can use AI to generate fake or misleading data. To overcome this obstacle, you need to ensure that your data is collected, stored, and processed securely, and that you use robust methods to validate, clean, and update your data regularly.
First of all, I think that AI is already very used in cyber security, and for a while. Data quality is a real barrier for any data-driven enabler adoption, particularly Machine Learning when most of the algorithms perform on a strict data quality to be able to accurately predict anomalies or recognize patterns. This being said, I noticed a pick on automating many segments of the data cleansing and structuring. The modernization of the data infrastructure gonna allow a pick of ML adoption, soon in areas where data quality was the barrier.
High-quality data is the lifeblood of AI's decision-making prowess. The digital equivalent of noise, distortion, and mirages in cs often mars this data. The presence of noisy, incomplete, or outdated data in cs is like navigating a minefield blindfolded, where one wrong step can lead to misjudgments. The threat of attackers using AI to contaminate the data pool with misleading information adds another layer of complexity, turning data validation into a high-stakes digital cat-and-mouse game. Treating data as a dynamic, living entity that needs constant care –securing, validating, cleansing,and updating –is crucial to overcoming these challenges. It's a meticulous process,but it's essential for ensuring the AI compass is accurate & reliable.
Vice President Global Chief Information Security Officer 2018 Global CISO of the year
Rapid growth Vs risk management, AI trust is a good thing, but control is a better one and a challenge. Artificial Intelligence security risks could grow faster than regulation's and companies' ability to keep up. Artificial Intelligence is a positive and promising innovation for cybersecurity. Conversely, cybersecurity is a crucial catalyst to make AI a sustainable productivity booster for companies. However, the rapid growth of AI-based software will challenge business and IT leaders to keep potential cybersecurity, privacy, and ethics issues in check.
Data quality is a paramount challenge in AI cybersecurity. Ensuring accurate, clean, and reliable data is the foundation for robust threat detection. In my experience, investing in data quality measures is a critical step toward a more secure digital landscape.
I want to comment on Mehdi's point about data quality. We need to all get comfortable that a new world of focus on data quality is here. The more we do with AI the higher importance there is on Data Quality...at some point we moved from Dirty In - Dirty Out to Garbage In - Garbage Out. Regardless of this the importance of data quality in AI functions be in ML, NLP, or GenAi cannot be ignored. To make AI algorithms work understood, managed, and monitored data is critical.
Another challenge for using AI in cybersecurity is the ethical and legal implications of its use. For example, AI can be used to automate decisions that affect the privacy, security, and rights of individuals and organizations, such as detecting and blocking malicious activities, or identifying and reporting vulnerabilities. However, these decisions may not always be transparent, fair, or accountable, and may raise questions about the responsibility and liability of AI systems and their operators. To overcome this obstacle, you need to follow ethical principles and best practices for using AI in cybersecurity, and comply with relevant laws and regulations that govern its use.
AI has become a solution looking for a problem. It has its place, just not everywhere. Everyone here has already discussed the ethical issues but the bigger problem here could be legal some the actual capability of the system. There is too much faith placed in systems because traditionally they have been very deterministic, they executed programmed rules and could be tested to a point of confidence. AI is the exact opposite and can be highly problematic if not properly understood. It is a probabilistic system, everything is grey even when the answer seems exact. AI systems need to be used only to augment the human who has to be responsible for making the final judgement call. There is currently too much blind faith.
Cyber risk accountability could also be improved using AI! How is that? C-levels and Ciso can now, Use AI tools like vendict.com to mitigate biases created by Cyber questionnaires that are part of the accountability process including the legal part. In that example, Vendict automates security questionnaire responses and provides red lines for any bias that might occur compared to the benchmark or standard, it eliminates human errors and secures the outcome accountability.
The ethical and legal implications of using AI in cybersecurity can be complex. AI companies will need to implement measures that allow them to remain compliant with the laws in the different jurisdictions, particularly when dealing with cybersecurity data that can come from a wide range of sources. From an ethical point of view, following best practices and standards is a great starting point. However, in cases with a grey area, companies should consider other methods to ensure ethical conduct. Ethical behaviour starts with the company culture and its internal ethical compass. Companies can also survey external stakeholders or perform bespoke research to understand the ethical basis of their actions.
The ethical and legal challenges surrounding the use of AI in cybersecurity are complex and multifaceted. From an ethical standpoint, one of the primary concerns is the potential for bias in AI algorithms. If these systems are trained on data that reflects existing biases or discriminatory patterns, there is a risk of perpetuating and even exacerbating these biases in cybersecurity decisions. Transparency and interpretability of AI models also pose ethical challenges, as the lack of clarity in understanding how an AI system arrives at a decision can hinder accountability and raise concerns about algorithmic accountability.
While privacy is a fundamental concern, it cannot exist without proactive cybersecurity. Given the rise of potential misuse of AI for adversarial attacks, companies need to harness AI for cybersecurity to meet their data privacy requirements. Major comprehensive privacy laws like the GDPR and CCPA already contemplate legitimate cybersecurity needs as lawful reasons to process personal data. Any AI cybersecurity company worth its salt should also limit analysis to back-end logs instead of invasive email or screen monitoring, de-identify data where possible, and provide privacy and security assurances to their customers by contract.
A third obstacle for using AI in cybersecurity is the skills gap. AI requires a combination of technical, analytical, and domain-specific skills, which are not always easy to find or retain in the cybersecurity field. Moreover, AI is constantly evolving and requires continuous learning and adaptation, which can be challenging for cybersecurity professionals who already face a high workload and pressure. To overcome this obstacle, you need to invest in training and education for your cybersecurity staff, and foster a culture of collaboration and innovation that supports the use of AI.
To build AI systems for cyber security, both AI professionals and cyber security professionals need to work together. Both the groups of professionals need to understand other group's work to effectively communicate with each other. Hence training need to be provided. Also training need to be well curated and targeted. I have often seen that the business SMEs are given trainings where they have Python course, an ML algorithm course and so on. This will not work. There needs to be a course which is well curated to align with the needs of the cyber security professionals. For example, cyber security professionals need to understand the AI project cycle, however they shouldn't be taught the maths behind complex algorithms.
The skills gap is a significant hurdle in AI cybersecurity. With rapidly evolving threats, there's a growing need for experts in this field. Upskilling and education are essential to bridge this gap. For everyone who is using #AI in #Cybersecurity, fostering talent should be a top priority.
Here is the unspoken truth: the cost of AI in cybersecurity isn't just a bill; it's a deep dive into our financial waters. Envision the excitement of cutting-edge tech, tempered by the reality of investing in staff training; each step demands more than dollars; it demands commitment. Ignoring this cost conversation risks puncturing not just our wallets but also our defense strategy. Resource Allocation Puzzle: The choice between investing in AI and other security measures challenges organizations to find the right equilibrium. ROI Uncertainty: While the long-term benefits of AI in cybersecurity are compelling, predicting the ROI remains uncertain. It's not just about the price tag; it's about the investment in the digital future.
We have to be mindful that the more emphasis that is given to any specific area of cyber, by cyber professionals, does not hinder or have a negative impact on the requirement for fundamental knowledge of an area of interest. This places the cyber professional into a "pigeon hole", which may impact both career prospects and overall expertise.
To overcome gaps in technical, analytical and specific skills, collaborative work and learning is required. Bridging the AI skills gap goes beyond improving individual learning. The problems we face in today's world require the union of different types of people and skills, capable of creating new solutions to global challenges in AI and cybersecurity. We need “skills capable of addressing AI and cyber security jointly and by different types of professionals beyond basic ICT skills - which is how many countries have defined them when incorporating 21st century skills into professional requirements and educational programs.
A fourth obstacle for using AI in cybersecurity is the threat of adversarial attacks. These are attacks that exploit the weaknesses or limitations of AI models, such as their sensitivity to small changes in the input data, or their lack of generalization or robustness. For example, adversaries can use AI to generate adversarial examples, which are modified data that can fool or evade AI models, or cause them to make false or harmful predictions or actions. To overcome this obstacle, you need to design and test your AI models with security and resilience in mind, and use techniques such as adversarial training, defense distillation, or encryption to protect them from adversarial attacks.
These attacks involve subtly altered inputs to deceive AI models, leading to incorrect conclusions or actions. The sophistication of such attacks is rapidly advancing, challenging the AI systems' ability to detect and respond effectively. This vulnerability can be exploited in critical security systems, undermining their reliability. Mitigating these threats requires continuously evolving AI models and incorporating robust adversarial training. However, this is an arms race; as AI defences improve, so do negative techniques. Addressing this dynamic and persistent threat is critical to effectively harnessing AI's potential in cybersecurity.
Tackle adversarial attacks by adversarial training. This involves deliberately feeding the AI system with tricky data during its learning phase. This is akin to training a boxer by sparring with tougher opponents to prepare for any move an adversary might pull. Additionally, implementing defense distillation, where the AI model is trained to generalize from complex patterns to simpler ones, has fortified systems against attack vectors. Encryption of data in transit and at rest further shields against malicious tampering. It's a continuous process of hardening defenses, much like evolving a castle's fortifications in medieval times to withstand ever-more cunning siege tactics.
The primary challenge of AI in cybersecurity is the constant evolution of cyber threats. AI's reliance on historical data hampers its ability to detect novel attacks that diverge from past patterns. Reinforcement Learning (RL), a field of AI, can aid cybersecurity. RL-based intelligent systems can continuously interact with the environment, learning to recognize and respond to evolving threats. For example, an RL system can analyze network traffic patterns and learn to identify anomalies that might indicate a cyberattack. The system iteratively refines its detection algorithms by receiving feedback on its actions. This continuous learning process makes RL-driven cybersecurity systems adapt to new strategies employed by cyber attackers.
Have you thought of it as a thriller movie?! Machines are making big decisions without humans in the loop. While automation is cool, think about a scenario where computers decide everything, even launching a cyber attack. Now, picture the plot twist: no human nod is required. It's not just a tech drama; it's a real-life thriller in digital defense. So, how do we keep the perfect balance between smart machines and good old human know-how in this cyber adventure? That's the gripping question cybersecurity buffs can't stop talking about. In this fast-paced tech thriller, finding the right harmony becomes the ultimate challenge, where innovation meets the wisdom of experience, and the future of cybersecurity hangs in the balance.
Adversarial attacks pose a formidable threat to the efficacy of AI in cybersecurity by exploiting vulnerabilities in the models' decision-making processes. Deliberate manipulations of input data, orchestrated by malicious actors, can induce misclassifications or oversights in threat detection, compromising the accuracy of AI algorithms. The dynamic nature of cyber threats exacerbates this challenge, demanding continual research to fortify AI models against evolving adversarial tactics. Mitigating this threat necessitates advanced algorithmic defenses, ongoing model monitoring, and adaptive strategies to uphold the integrity of AI-powered cybersecurity systems in the face of persistent and sophisticated adversarial endeavors.
A fifth obstacle for using AI in cybersecurity is the lack of trust and adoption. Despite the potential benefits of AI for cybersecurity, many stakeholders may be reluctant or resistant to use it, due to factors such as lack of awareness, understanding, or confidence in its capabilities and performance, or fear of losing control, autonomy, or jobs. To overcome this obstacle, you need to communicate and demonstrate the value and effectiveness of AI for cybersecurity, and involve and empower the users and beneficiaries of AI systems, such as employees, customers, or partners.
Cyber security impact human rights like right to privacy, right to data security etc. For such sensitive topics, I believe humans will be making the final decisions. AI will be the support system for humans (cyber security professionals in this case). This message needs to be conveyed to cyber security professionals, and overtime the concerns of loss of autonomy, controls, job etc. will subside. However in parallel the AI systems should start delivering value, AI system can run in parallel in the beginning or partially deployed in one country only. Once the value from AI systems is substantial, and the messaging is clear that AI is providing support instead of taking over, adoption will increase.
In cybersecurity, while focusing on challenges, let's also recognize AI's power. AI is more than a helper, it's a game changer, offering an increase in efficiency. It quickly identifies and helps mitigating critical vulnerabilities in apps, which are potential hacker entry points. Machine learning makes it easy to teach systems to find and stop these threats. AI acts like a digital detective, analyzing malicious code and strengthening our systems before damage occurs. In this field, AI is not just a tool, but a vital protector against unknown threats.
The obstacle lies in the lack of the nuanced understanding and human wisdom required to assess the true importance or weight of a specific risk. AI systems excel at processing vast amounts of data and identifying patterns, but they may struggle to comprehend the broader context or subtle intricacies that a human would consider. Human experience and intuition play a crucial role in evaluating the gravity of a cybersecurity threat, taking into account factors like business priorities, potential consequences, and the evolving nature of cyber threats. Therefore, human judgment must always be integrated alongside AI capabilities to adopt a more comprehensive approach to cybersecurity.
The opaque nature of how AI prioritizes the decision-making process and judgments is not fully explainable. Advanced ML models, such as deep neural networks, operate as complex "black boxes" in the sense that it can be hard to interpret and explain their internal workings. If the decision-making process is not transparent, it becomes difficult to hold the AI accountable for its actions, and users may be hesitant to rely on or trust the system. Moreover, models can inadvertently learn biases present in the training data. Without clear choice indications, it becomes arduous to identify and address biases within the model. Documentation showing the architecture, parameters, & and decision-making process of the model would help tremendously.
While we all are focusing on cybersecurity challenges, let's pivot to the positive side. Here's a valuable insight we should consider: AI isn't just a helper; it's a powerhouse. Imagine a 40% boost in effectiveness, swiftly detecting and fixing vulnerabilities in applications and databases. These aren't just minor glitches; they're potential entry points for hackers to access private information. With machine learning, teaching systems to spot and neutralize these threats becomes seamless. Think of AI as a digital detective, examining malicious code and delivering solutions to fortify our systems before any harm is done. In cybersecurity, AI isn't just a tool; it's a silent defender against unseen dangers.
There are very few cyber security attacks (and I am thankful for the same :) ). I am saying very few cyber security attack cases from the data perspective where we are looking at %age of records with cyber security attacks as compared to %age of records without cyber security attacks This makes the data skewed in favour of cases where there are no cyber attacks, as there are only handful cases of cyber security attacks. One needs to find ways to remove this skewness of data, and there are multiple ways to do it. One can duplicate the cyber security attack cases, use methods like SMOTE etc.
Using AI systems for cybersecurity is important, allowing you to process large quantities of data and detect patterns. However, you should be careful of AI drifts. For your AI system to perform well and deliver results, you should implement robust quality assurance measures to ensure that your input data is reliable and output consistently offers high precision. You may experience AI drifts organically, or bad actors may spoil your data or exploit vulnerabilities in your system to manufacture an AI drift that can hurt you.
To discuss the 'obstacles' in using AI in Cybersecurity, one must consider Cybersecurity as the frontline defense not only for a Digital Enterprise (economy, nation, etc.) evolving into a Cognitive Enterprise (economy, city, nation...science, education... and beyond). Now, take a minute to contemplate! At this very moment, Cybersecurity is perhaps the most underfunded, understaffed, and undervalued discipline, field, or industry - supported by pillars like people, processes, and technologies. Imagine that those carrying the weight of the future on their shoulders and minds would be the first to welcome AI as an ally, a highly trusted partner, provided such AI exists.
In addition to the factors already discussed, it's important to consider the dynamic nature of cyber threats and the evolving landscape of AI technology. I am advocating for a proactive approach to continuously update and adapt AI systems in cybersecurity. This includes staying abreast of the latest cyber threats and AI advancements, as well as fostering a culture of innovation within the organization. Furthermore, emphasizing collaboration between AI developers and cybersecurity teams is crucial for a more integrated and effective defense strategy. It's essential to keep in mind that as AI becomes more sophisticated, so do the tactics of cyber adversaries.
In addition to what's mentioned above, AI systems require substantial computational resources, especially for processing large volumes of data in real-time. Ensuring these systems are scalable and performant under varying loads is a major technical challenge. #ArtificialIntelligence #DataScience #TechChallenges