Database security is seriously threatened by insider account privilege assault, which results in data breaches. The dynamic nature of cybersecurity means that traditional intrusion detection systems (IDS) have significant rates of false negatives and false positives. By combining the Danger Theory (DT) with the Negative Selection Algorithm (NSA) method of the AIS, we offer a novel database intrusion detection technique that resolves these problems. Our algorithm uses a self-learning mechanism with the goal of improving detection coverage and decreasing false positives. The signatures of intrusions that have already been discovered are utilized by this mechanism as detectors for upcoming detection procedures. The integration of the Danger Theory (DT) model with the Negative Selection Algorithm (NSA) algorithm, which provides flexibility and efficiency, addresses the weaknesses of the present IDS. The experimental findings show that the algorithm is effective in achieving both a high detection activity and low false positive rate. The suggested approach guarantees the confidentiality, integrity, and availability of delicate data while also enhancing the capacity to identify insider threats and preventing data breaches. In addition, we present an extensive overview of associated research avenues, providing an integrated viewpoint on the suggested algorithm and its consequences within the wider database security context.