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Problem Statements: Risk Management with AI and Machine Learning in Intrusion
Detection Prevention(IDP)
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Problem Statements: Risk Management with AI and Machine Learning in Intrusion
Detection Prevention(IDP)
Data Quality and Integrity Issues
AI and machine learning models that are used in Intrusion Detection and Prevention
(IDP) are data-intensive and thus require high-quality and accurate data. In practice, the data
available to the model can also be of very low quality; for example, missing, incomplete,
inconsistent, or even deliberately malicious data and would greatly impact the accuracy of the
model being created. This tends to lead to an increased rate of false positives (marking harmless
activity as risky) and false negatives (ignoring real threats). The maintenance of data integrity is
a rather difficult but unavoidable task for threat mitigation since the models rely on the
availability of accurate and up-to-date data.
Adversarial Attacks on AI Models
Current AI models in IDP are susceptible to adversarial attacks whereby the attacker
changes the input data to fool the model into misclassifying or completely ignoring the malicious
activity. Such changes, which are hardly noticeable to people, can result in the inability of the
system to recognize real threats and even cause false alarms. This susceptibility is a very high
risk because attackers armed with advanced techniques can take advantage and penetrate through
the gaps that such attacks create, making it paramount to strengthen the integrity of the AI
models to such adversarial inputs.
Model Interpretability and Explainability
One of the major challenges of using AI models in IDP systems is that they are complex;
thus, the decisions made by the systems are often hard to explain to security analysts. This
‘blackbox’ characteristic makes it difficult to verify and refine the model as well as justify or
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present decisions to interested parties or meet compliance needs. The limited ability of the users
to decipher the rationale behind the model's decisions undermines confidence in the decision-
making process and decreases the model's overall efficiency due to the inability of the user to
explain one's actions based on the outputs, which they do not fully understand how they come to
be.
Dynamic and Evolving Threat Landscapes
The cyber-security environment is highly dynamic in the sense that new types of attacks
and strategies are frequently being introduced in the market. AI models in IDP systems, which
often use past data on their training, can be slow to identify new threats. The constant change in
the nature of cyber threats creates the need to constantly update and train models to respond to
the ever-evolving threat landscape, which is a challenge when attempting to prevent complex and
novel attacks.
Resource Limitations and Scalability Challenges
Sustaining and implementing AI-based IDP systems may be expensive in terms of both
computational and maintenance resources. Fundamentally, these systems must process
considerable amounts of information to operate effectively, and this can be a problem for small
companies with few resources or technical capacity. The challenge of scaling the system to
handle larger volumes of data without a degradation of performance becomes an issue for the
system's overall feasibility and longevity, especially for small organizations.
Privacy and Compliance Concerns
AI incorporation in IDP systems includes data gathering and processing, hence making
privacy and regulatory issues doubtful. While threat detection is important, organizations also
have to consider privacy and regulatory compliance so as not to violate user rights such as those
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under GDPR and CCPA. If these concerns are not addressed, there could be potential legal
consequences; the company’s reputation might be tarnished; and users would lose confidence in
the system. It is therefore important that IDP systems privacy is protected and that they are in
compliance with the law.
