Total Reliance on AI – Relying solely on AI for threat detection can lead to complacency. AI systems might miss novel threats or sophisticated attacks.
False Sense of Security and Blind Spots – AI systems are typically trained on historical data. If attackers use new methods not included in ‘training data’, AI might fail to recognize them
Quality of Data & Dependency – AI’s effectiveness depends on quality of ‘training data’. Incomplete, biased, & outdated datasets can lead to inaccurate threat detection.
Data Breach and Privacy Risks – Using sensitive or improperly anonymized data for training can expose sensitive information which may violate local privacy regulations. Regulatory frameworks often require clear explanations of decision-making, which black-box AI models may fail to provide.
Model poisoning & Adversarial Attacks – Attackers may craft inputs designed to deceive AI models, continually adapt; slightly altering malware to evade AI detection & can corrupt AI ‘training data’, which may result in flawed models ignoring specific threats or even create false positives, too many of those may overwhelm analysts, leading to alert fatigue and missed real crucial threats. Additionally, false negatives may occur when AI systems fail to detect subtle & novel attack patterns
High Resource Requirements (Cost Factor) – Training & running AI models i.e., Building/maintaining AI systems require specialized & skill resources, which may not be readily available in all cybersecurity teams and may be a cost- barrier for smaller orgs.
Evolving Tactics – Dynamic Threat Landscape – Cybercriminals continually adapt; to stay effective, AI models need constant retraining. Failure to update models can leave systems vulnerable.