Today organizations use various methodologies to protect their digital information and minimize business risks:
1. Firewalls, virus scanners, SIEM, IDS, IPS and vulnerability scanner & detection tools.
2. Conduct internal audits, external audits with risk assessment specialists.
3. Implements compliance as per regulatory laws like GDPR, HIPAA, GLBA, SOX etc.
4. Implements standards like ISO, SOC etc.

Still every day we hear about many Fortune 100+ companies getting cyberattacks and losing their confidential information and their data available in black market for sale.

Why do these types of threats not be prevented or minimized in spite of all the methodologies used by organization?

1. Limited technical knowledge in defining and creating the roles & rules in their firewalls etc.
2. The auditors may not be fully aware of technological back-gate weakness.
3. Compliance enforces the protection but does not suggest how.
4. Standards cannot be forced to be followed and again it depends on audit.

What are the other options the organization should think about is Augmented Application Security (AAS), where the AAS use Data Science with Artificial Intelligence(AI), Machine Learning(ML) and Deep Learning(DL) in their application design stage or even after the application moves to production, which means the protection does not end with traditional way of roles, rules, profiles, access controls, segregation of duties and event logging.

In AAS, application behavior is coded with data science logic, which means the software components within the application are monitored and it will take action of stopping that service, malware spread and report the anomalies to the concern, Also the AAS logic is designed as per business logic and enforce compliance and prevent cyber attacks, it is self learning and keeps accommodating to new standards and behavior by informing the concerned.