Abstract
Vulnerabilities are the cracks in IT
landscape through which hackers and other malicious users gain unauthorized access
to the underlying systems and perform nefarious activities. These attacks can
cost dearly to the company leading to financial and legal impacts. With the
attackers getting smarter and finding new ways to hack, it is a continuous and
tedious job for the security division to wade away these attacks and protect
their applications. Leveraging AI and machine learning the security team cat
have a better understanding of their systems and be able to identify and plug
any gaps found. Using machine learning the team can have a complete knowledge
of their landscape to understand the functionalities and with AI they can
identify the gaps in the system that might potentially be a target for hackers
to creep into the system. With careful design considerations and definitions AI
can be a boom to the security division for performing various security checks
in various phases of the project and support to find problems with the system
and take corrective remedial actions. It can also be used to scan through the
Internet and other support centers to find similar situations and recommend
patches and other practical solutions for the issues identified thereby
directing the team to traverse in the right path. There are several steps and techniques
that can be followed to accomplish this which will be seen in detail in the
following document.
Keywords: AI, Application vulnerability scanning, Data security,
Machine learning, Regression scanning, Application landscape monitoring, Application
security, Iterative analysis, Automated security data gathering.
1. Introduction
By going digital every organization has
moved their data and operations into a machine thereby automating their process
and by providing access to anyone around the world with proper access. However,
it comes with the cost where unauthorized users can creep through the gaps in
the system causing damages to the company these damages can reach anywhere from
stealing corporate data or to cripple the entire system with the intent of
disrupting the operations or extortion. Hence every organization has a resolute
team and systems working around the clock to monitor their service and other
applications to see if there are any such attacks and take timely and decisive
actions against it. However not every problem can be identified since as the
landscape grew this attack surface increases proportionally leaving certain
areas susceptible to such attacks. Hence there needs to be a systematic
approach in dealing such problems and leveraging every technology and options
available in the market to secure once data and other proprietary items from
being stolen or corrupted. These vulnerabilities can be in any layer and
manually scanning through them to find any problems is naturally impossible
hence leveraging AI and machine learning to do these scanning on behalf of the
security team is a blessing to quickly scan through and receive necessary
guidance for remediating these problems.
2. Challenges with Vulnerability Scanning
With advent of globalization and open
market systems, it has become an inherent need for every organization to expose
their systems to the outside network. With divorce operating model where
employees work from every corner of the world the ability to connect these
systems and work around the clock for the growth of the organization has been
very fruitful. However, this has introduced new challenges with respect to data
security since the systems are exposed to the outside world and are secured by
various algorithms and authentication methodologies. While these security
measures have helped the organization to a great extent there are some
technically savvy hackers who can bypass the security and gain access to their
internal systems and with several layers of infrastructure and platforms a
vulnerability in any one of the layers might compromise the whole architecture.
Hence, its particularly important that these scans are done on every system
frequently. But there are several factors both internal and external that pose
challenges.
2.1.
Size and complexity of IT landscape
The first hurdle is the IT landscape by
itself. Most organizations do not use one single application for their entire
operations, but a combination of various applications and solutions integrated
by a middleware or a complex networking system. This system might how they run
architecture and framework and scanning each application for vulnerabilities
would mean a clear and deep understanding of these applications itself. To
accomplish this organizations, depend upon vendor documentation and expert
knowledge, but these details are accurate only to a certain extent and there
could be several rouge codes and unexpected additions that would significantly
deviate from what is recorded and documented. Hence it is impossible to have
one generic scan solution for identifying all the cracks in the system.
The complexity of the architecture can
also be a huge challenge. In a typical IT environment that usually is a server,
database and an application that run to help run a business. There are
different scans and different identification methodologies needed to find
problems in each of these sections hence that needs to be different methods of
scanning addressing a particular segment of the IT architecture.
2.2.
Application Changes
With evolving technologies and
organizations adapting to the more modern and advanced versions, The IT
department constantly undergoes changes and it is extremely hard for scaling up
these scans to meet with the modern technology. Also, the need for frequent
scans means there is an exceedingly small window between the upgrade and the
tuning of the scanning algorithms to be in line with the latest version which
is practically impossible. Additionally, if the vulnerability is identified in
sections of the out-of-the-box product kappa the organizations are forced to
rely on the vendors to provide patches and security updates to address the gaps.
This might take a while before such patches are developed, tested and released,
leaving the systems exposed for attacks.
Even with custom coding, which the
organizations have complete control over, there are challenges in scanning for
any problems. An ineffective and open-ended coding might but create a crack in
the system that can be exploited. However, to confirm if there is in fact a weakness,
there needs to be a clear understanding of the requirement and the design.
There could be a conflict between what is considered as a vulnerability and
what is needed by the organization for its functioning thereby putting it in a
stalemate unable to deduce if there is a problem or not. While security Trump's
the functionality that was always a grey area where each of the teams need to
compromise to move ahead, leaving the weaknesses dormant and ready to be
exploited.
2.3.
Scan Frequency
Security threat to the IT systems is a
constant and a continuous one for every organization. Especially critical
industries such as banks, defense and government institutions face hundreds of
such attacks on a daily basis. It is important that these attacks are thwarted
every time since even a single breach could be a costly one. To effectively do
that the systems have to be scanned on a regular basis such that the gap
between the introduction of a vulnerability and the time it is identified is
kept as minimum as possible. However, this would mean that that needs to be
high performing high capability processors running all the time and scanning
systems taking up a significant amount of time and resources for performing
this activity. These scans have to be specially triggered during and after a
project goes live, not just the product that is going like but any application
or systems surrounding it to find if there are any weaknesses introduced. While
large, well-funded organizations can afford such scans small and medium scale
industries cannot, but the threat is no less significant.
And there is also a factor of human error.
There is always a chance that certain aspects will be overlooked or neglected
by manual intervention which might prove to be a fatal flaw. As part of the
regression testing that is always a chance that either the testing will be
skipped or will run based upon certain assumptions which might no longer hold
good causing some weaknesses gone undetected. Certain assumptions and reliance
on other systems such as firewalls or VPN to take care of the security might
leave the cracks in the applications unaddressed.
3. AI in Vulnerability Scans
By Using AI and watch most of the
challenges mentioned above, if not all can be successfully addressed. The
technology has advanced so much that the modern AI systems can read the entire
it structures with minimal or no human intervention. This can be a powerful
tool that can aid the security team to constantly monitor, scan, collect data
and analyze them to identify and provide relevant suggestions to tackle the
issues. Coupling this with bots, makes it at deadly and effective defense
system that can protect from external threat and constantly check and fix the
internal systems. To achieve this there are a few preparatory steps that need
to be performed to make AI equipped to protect underlying systems.
3.1.
Understand the Landscape
To check the quality of the system EA
needs to first understand the system. It needs to and review the system to know
the functionalities of the system individually and its relation to other
applications both within the network and outside. This would help the AI system
recognize what should and should not be exposed to the outer world. For instance,
an application storing employee data, the system needs to know how this
application operates and used both internally and externally. The service it
provides determines what data exposed is considered a breach and what not. This
forms the basis of the entire scan. Anything that violates these rules would be
considered as invisibility and a red flag raised for the appropriate team to
take an action. The ideal way would be to feed AI with proper documentation associated
to the product, however, there are many instances where the documentation is
vague or incomplete rendering it useless. Hence the AI and machine learning
systems should be hooked to these applications to continuously read the system
of its daily operations to have a comprehensive knowledge of the application
and its related systems, therefore possessing the day-to-day knowledge of the
system in question. This activity can also throw light on the on-ground
functioning, which can be further reviewed by the operational committee for any
operational gaps that can be remediated.
3.2.
Define Security Laws and Priority
The next part is the definition of
security rules and regulations that every application needs to adhere by. These
last can be dictated either by the government or by industrial standards or
within the organization to maintain their reputation and the standards in the
market. These rules can be hardened fast that can never be broken or guidance
which the development and maintenance teams need to keep in mind unless it does
not seriously affect the business operations. These rules can also have various
levels of adherence that is the rules can be relevant for the entire
corporation or for a certain set of applications. There should be careful
definition of this rules to ensure that they do not conflict with each other
and in case they do there needs to be a certain amount of priority provided to
clearly define which overrides. These rules serve as the guiding metrics for
all the scans and the applications that they cannot needs to adhere to these
rules necessary if not flags are raised depending upon the criticality of the
vulnerability. Care should also be taken that these rules are neither too
strict that it is practically impossible to sustain such a standard or too
lenient that a lot of vulnerabilities do not get captured as part of the
scanning process.
It is also important to know that not
every system needs the same level of security. Certain applications do not hold
such sensitive data nor Part of the core network that any breach into such
system might Causeway a grave damage to the organization. While any and all
threats need to be eliminated, the organizations need to take into
consideration the cost factor while defining such rules. Having a uniform code
of security might not always be the best course of action. Hence that needs to
be detailed analysis of the current IT structure, the future scope and any
other factors in deciding these rules.
3.3.
Scope and Frequency
Whit the Systems analyzed and understood
and rules defined, the next logical step is to design the scans themselves.
Realistically this is an extremely complex process since the landscape can have
diverse set of applications running with different operating systems in
different capacities. While the rules to adhere might be the same the ability
to identify vulnerabilities based upon these rules would mean different forms
of scanning. For example, to identify an authentication vulnerability in a
server the process would be completely different from the scan that will be
done in Oracle applications or database. Similarly, each application might be
quoted using different languages such as PLSQL, Java, python, etc. and scanning
these codes would mean a knowledge of these languages and the API's that they
usually use and their validation, for example, a piece of code could be using
and depreciated API from a Java cool so this can be called out as a
vulnerability only with the knowledge of the valid APIs in the Java version
that is being used.
Earlier, the scans can either be a
continuous one or a periodic one. For the most critical systems and
applications it is recommended that these scans happen in the highest
frequency, continuous if possible. Using bots coupled with the knowledge gained
by AI can be an effective combination. Bots are tireless piece of software that
is designed to do a specific set of activities. These come in handy in
situations where application testing is needed in a much frequent scale.
Continuously scanning through the applications and collecting data the security
team would have a great wealth of data to perform the analytics and identify
appropriate solutions. This comes in handy especially in situations where the
vulnerabilities are identified as part of an out-of-the-box product where the
company needs to rely on the vendor to provide a solution. Until then using the
data gathered the company can take educated and timely decision to overlay the
burden on other systems such as network and VPN to take care of security until
the problem is addressed. Though might not be an ideal solution this might be a
quick fix solution to avoid any unpleasant situations.
The abilities discussed above are merely a
fraction of what AI can do for the security of an organization. With the
machine learning evolving every day the capabilities have increased multiple
folds enabling the system using these facilities to design a comprehensive and
a robust solution. While it is true that the hackers are getting innovative by
the day with careful planning and design and proper rules in place these
attacks can be prevented or at minimum controlled that by avoiding any
significant financial or legal issues. Several mundane and a repetitive task
have been taken over by AI and bots where the margin of error is expected to be
minimal or none. However, the effectiveness of these systems depends upon the
rules and the landscape of the organization.
4. Conclusion
While it is true that not all cracks and
weaknesses can be identified and addressed, with the help of AI and bots a sizable
number of these problems can be identified and addressed. While AI and other
latest technologies can assist in tackling these problems, it is extremely
important that the underlying architecture be designed to have layers of
security based on the criticality of each application. Also, every application
owner needs to accept the fact the security is everybody’s responsibility and
not just rely on outer layers to secure them since the attack can originate
internally too. In the current digital world, security is not stopping all the
attacks, which is impossible by itself, but the ability to respond quickly by
taking necessary counter measures and recover with the least impact and time.
By leveraging the latest technologies and solutions in the market, these is no
doubt that any organization can achieve this.
5. References