Application-Security-Manager-How-AI-Changed-the-Game.pdf

The average cost of a data breach in 2024 has spiked to $6.5 million per incident, a 150% increase on 2020.

Astonishingly, around 50% of these attacks are estimated to have been either AI generated or developed.

85% of cybersecurity professionals attribute the increase to the use of AI by bad actors. This sudden rise in AI enabled cyber attacks is also in stark contrast with the long documented cybersecurity skills shortage, putting ever more pressure on cybersecurity management.

This extreme pace of change has transformed the role of application security managers, who are ultimately responsible for the holistic security of an application.

In modern IT, that application will typically be a SaaS or a custom software solution, all hosted on cloud.

And in terms of application security, this is likely to be coming under growing AI-generated attacks such as smart automated vulnerability scans with highly contextual input injections, using GenAI for advanced personal phishing attacks and generating customized trojans.

The role of application security manager has been impacted by these meta-trends, growing strategic and complex as the variety, scale, and sophistication of cyber attacks have grown exponentially.

We are living in a world where anyone can buy live 2FA-protected session tokens for internet banking users of large banks for less than $10 on the dark web. AI is clearly playing an increasing and accelerating role here.

AI can be used by bad agents, for example to generate deep fake fraud or to create customized malware at scale, or by good agents, such as those using AI-powered tools for account monitoring and fraud prevention.

1. Application Security
Manager : How AI
Changed the Game
This ar ticle explores how AI has revolutionized application security , providing
insights into the latest advancements and their impact on safeguarding
digital assets .
by Elena Barkovskaya

2. AI 's Impact on Application
Security
1
Enhanced Threat Detection
AI algorithms can analyze vast amounts of data to identify suspicious
patterns and predict potential threats , enabling proactive security
measures .
2
Automated Security Testing
AI - powered tools can automate security testing processes ,
identifying vulnerabilities and weaknesses in applications more
efficiently than manual methods .
3
Real - Time Threat Response
AI can analyze and respond to threats in real - time , adapting to
evolving attack methods and minimizing damage .
4
Improved User Authentication
AI - driven behavioral biometrics can enhance user authentication by
analyzing user behavior patterns , making it more difficult for
unauthorized individuals to gain access .

3. The Rise of AI Cyber Attacks
The use of AI in cybersecurity is a double - edged sword . While AI can
enhance security , it also empowers attackers to develop more sophisticated
and evasive attacks .

4. Security Measures in the Age of AI
Data Encryption
Encrypting sensitive data makes it
unreadable to unauthorized
individuals , even if they gain access
to the system .
Multi - Factor Authentication
Requiring multiple forms of
authentication , such as passwords ,
biometrics , and one - time codes ,
adds an extra layer of security .
Regular Security Updates
Keeping software and systems up -
to - date with the latest security
patches is crucial to protect against
known vulnerabilities .

5. Behavioral Biometrics : A New
Frontier
Behavioral biometrics analyzes user behavior patterns to verify their identity ,
providing a more secure and user - friendly authentication method .

6. Legal Cybersecurity : Navigating the Complex
Landscape
Legal cybersecurity involves understanding and complying with relevant laws and regulations to protect sensitive data
and ensure responsible use of technology .

7. Combatting Fake Registrations
Fake registrations can harm businesses by diluting user data , impacting marketing campaigns , and creating security
risks .

8. CrossClassify : A Solution for
Fake Account Detection
CrossClassify offers a comprehensive solution to combat fake registrations ,
helping businesses protect their platforms and ensure genuine user
engagement .

9. How CrossClassify Works
1
Data Collection
CrossClassify collects
data on user behavior ,
including registration
patterns , device
information , and
interaction history .
2
AI Analysis
AI algorithms analyze the
collected data to identify
suspicious patterns and
potential fake accounts .
3
Risk Assessment
CrossClassify assesses
the risk associated with
each user based on the
analysis , flagging
potential fake accounts
for fur ther investigation .
4
Actionable Insights
CrossClassify provides
actionable insights and
recommendations to help
businesses mitigate the
risk of fake registrations .

10. Scale Your Business Securely
By implementing robust security measures and leveraging AI - powered
solutions like CrossClassify , businesses can scale their operations securely
and confidently .