Towards Automated Information Security Governance
Ariane Trammell
, Benjamin Gehring
, Marco Isele
, Yvo Spielmann
and Valentin Zahnd
Zurich University of Applied Sciences ZHAW, Winterthur, Switzerland
Stadt Winterthur, Winterthur, Switzerland
StepStone Group, Zurich, Switzerland
Secuteer GmbH, Zurich, Switzerland
Security Management, Security Controls, Governance Risk and Compliance (GRC), Automation.
Securing a company is not an easy task. Many organizations such as NIST, CIS, or ISO offer frameworks that
offer comprehensive security measures. However, those frameworks are generally large and require expert
knowledge to be tailored to a given organization. Since such experts are rare, we propose an automated solution
that selects security controls and prioritizes them according to an organizations need. We performed initial
steps towards the implementation of the proposed solution by evaluating how Natural Language Processing
can be used to select security controls that are relevant for the assets of a company and by showing that we can
prioritize the selected controls based on the current threat landscape. We expect the proposed solution to be a
major benefit for all organizations that intend to improve their security posture but are limited in specialized
According to the report on ”SMEs and cybercrime”
by the European Commission (Commission, 2022),
28% of SMEs have experienced cyber crime in the
last 12 months and 70% are concerned about risks as-
sociated with cyber security such as hacking, phish-
ing, or malware. There are numerous opportuni-
ties for these companies to improve their protection
against cyberattacks. In order to achieve system-
atic improvement, various standards and best prac-
tices are available, such as the ISO 27001 stan-
dard (ISO, 2022), the NIST Cybersecurity Frame-
work (NIST, 2018), the CIS Critical Security Controls
(CIS, 2021) or the BSI IT-Grundschutz-Kompendium
(BSI, 2023). These standards all offer good opportu-
nities to sustainably improve a company’s IT-security
and are also partly integrated into so-called GRC tools
(Governance, Risk and Compliance) such as (servi-
cenow, 2023) or (sai360, 2023), which are intended
to provide an overview of the security level achieved.
However, the standards have a very large number (in
some cases more than a thousand) of requirements
that are called security controls. For this reason, IT
security specialists are required for the implementa-
tion, who prioritize the security controls for an or-
ganization and also specify how these are to be im-
plemented exactly. However, as can be seen from
numerous media reports, it is precisely these secu-
rity specialists who are currently lacking. This pri-
marily affects small and medium-sized organizations,
which have to secure their IT without dedicated secu-
rity specialists. For them, there are now also some ap-
proaches that specifically address SMEs. For exam-
ple (NIST, 2023) offers a collection of references for
small organisations. Some are very broad in the form
of a small fact sheet (such as (CISA, 2018)) while oth-
ers try to customize their advice to the organization
with a couple of high level questions (such as (FCC,
2023)). However, those offers still require significant
security knowledge to implement the suggested mea-
sures as they are not tailored to an individual company
or current threat landscape. Additionally, they are not
supported by general GRC tools, which mandates a
higher involvement of IT security professionals.
This lack of suitable solutions for SMEs com-
bined with the lack of IT-Security professionals im-
plies that often ad hoc security solutions are imple-
mented rather than a standard is followed. The level
of security achieved with this method depends heavily
on the knowledge and initiative of individual employ-
ees. This means that the management level has no
Trammell, A., Gehring, B., Isele, M., Spielmann, Y. and Zahnd, V.
Towards Automated Information Security Governance.
DOI: 10.5220/0012357500003648
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 10th International Conference on Information Systems Security and Privacy (ICISSP 2024), pages 120-127
ISBN: 978-989-758-683-5; ISSN: 2184-4356
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
overall view of the state of IT security in their com-
pany and can only control it insufficiently. In order to
advance the current state of the art, we propose a sys-
tem that automatically recommends customized secu-
rity controls to an organization. The focus here is on
small and medium-sized organizations that currently
cannot afford or do not want dedicated security spe-
The reminder of this paper is organized as follows.
In Section 2 we discuss how our approach differs from
related work. In Section 3 we introduce our over-
all system architecture. In the next two Sections we
look into two building blocks of the overall architec-
ture namely security control selection (Section 4) and
prioritisation (Section 5). For both building blocks we
show implementation possibilities and initial evalua-
tion results. In Section 6 we conclude our paper with
a review of the achieved results and the identification
of further work.
In this related work section we look at research
projects that work towards an automated selection or
prioritization of security controls. CIAM (Llans
2012) is an approach in which security controls are
given weights in various categories by IT security spe-
cialists. Based on these weights, the controls are then
prioritized. This inadequacy is taken into account in
(La, 2023), where the weighting of the controls is
made on the basis of real attacks. Here, the focus is
on the technical security controls and not on those of
processes. It is also not possible to adapt the scope
of the controls to the size or sector of the correspond-
ing organization. Thus controls might not be appli-
cable if the affected infrastructure is not used, or im-
portant controls might be missing for an organization
that uses specialized infrastructure.
Other approaches such as (Barnard and von
Solms, 2000), (Yevseyeva et al., 2016) and (Neubauer
et al., 2008) try to find the relevant controls for a
project or IT-asset from a standard such as ISO or
NIST. In all approaches, a lot of security expertise
is still needed, for example, to capture security re-
quirements, set weights, or weigh possible solutions
against each other. Our system, on the other hand,
is designed to suggest and prioritize relevant secu-
rity controls without the need for IT-security experts.
For this purpose, information from various sources is
combined to automatically create a digital twin of the
organization. This data is then used to select the con-
trols that are relevant for an organization and in a sec-
ond step those controls are prioritized.
Figure 1: Overall System Architecture.
To build a tool that supports SMEs with the selection
and prioritization of appropriate security controls, we
propose the architecture shown in Figure 1.
The basic idea is to combine openly available data
and company internal knowledge to automatically se-
lect and prioritize security controls.
Security Control Selection. In order to se-
lect controls that are relevant for an organization, as
much as possible has to be learned about this organi-
zation. To this end openly available external informa-
tion is combined with company specific knowledge to
build a digital twin of that organization. The infor-
mation analyzed might range from certificate trans-
parency logs (Transparency, 2023) to gain an insight
into the web presence of a company over question-
naires to network scans of the organization where ap-
plicable. The control selection process uses the in-
formation gathered in the digital twin to select rele-
vant controls from established security control cata-
logs such as provided by NIST, CIS, BSI, etc.
Security Control Prioritization. The prioritiza-
tion of security controls is based on the relevant se-
curity control selection and information specifying
the current threat landscape. This information could
be obtained by a human assessment or automated by
analysing threat feeds.
Risk Assessment. In order to be able to assess
the overall risk related to cyber security of an organi-
zation, the implementation status of the security con-
trols needs to be assessed. This assessment should be
done as far as possibly automatically, however, this
is not for all controls possible. For example, it is
Towards Automated Information Security Governance
comparatively easy to automatically assess whether
all client devices are patched, but it is difficult to as-
sess whether an appropriate business continuity plan
is in place. The final risk assessment combines the
implementation assessment of the individual security
controls with the data available in the digital twin.
A dashboard containing the questionnaire, the pri-
oritized controls, a control assessment possibility as
well as the resulting risks allows the responsible per-
sonnel to plan security measures systematically and
to see the effects in the resulting risks.
In the next two sections we look into the control
selection and control prioritization in more detail.
One approach for the selection of appropriate controls
is to make a mapping between IT-assets such as hard-
ware or software components and security controls.
This mapping has the advantage that the compliance
of every IT-asset can be assessed individually. For
many controls this is beneficial, since a control might
only be relevant to a subset of the IT-assets and from
that subset it might only be implemented by another
subset. For example, a control that asks for a timely
patching of devices might be implemented for client
devices but not for IoT devices.
We explored natural language processing (NLP)
as a mechanism to perform the matching of IT-assets
to security controls. Since both IT-assets and secu-
rity controls are described by natural language, NLP
is an adequate mechanism to perform this mapping.
We explored two different approaches, a tag-based
approach and a description-based approach.
Tag-Based Approach. In the tag-based ap-
proach, NLP is used to generate tags from the control
and asset descriptions. If a tag of a control matches a
tag from an asset the control is applicable for that as-
set. The process to obtain tags differs slightly for con-
trols and for assets, as assets are typically described
by a set of properties but no long verbal description
whereas controls typically have a longer verbal de-
scription. The method is shown in Figure 2a.
Description-Based Approach. The description-
based approach directly compares the description of
an asset with the description of a control. As assets
generally doe not offer a long description, this de-
scription has to be obtained in an additional step, such
as looking up the corresponding product description
in the Internet. The matching is performed by NLP
to extract the similarities between descriptions of as-
sets and the controls. If a particular similarity value is
reached, a security control is applicable for the given
(a) Tag based matching.
(b) Description based matching.
Figure 2: NLP Based Matching Methods.
asset. This approach is shown in Figure 2b.
While the tag-based approach uses keywords to
match controls and assets, the description-based ap-
proach uses the descriptions themselves. Both ap-
proaches have in common that they rely on an asset
inventory that contains all IT-assets relevant for an
organization. If such an inventory is not available,
it would have to be compiled as a first step. In or-
der to automate this process, techniques such as net-
work scanning or accessing information from soft-
ware management tools could be used.
We implemented and evaluated both approaches
with the security controls provided by the NIST
Framework (NIST, 2018) and the asset inventory of
our organization.
4.1 Implementation of Tag-Based
The tag-based approach follows the following steps:
a) keyword extraction, b) clustering, and c) topic
modeling. Each of those steps is defined in the fol-
lowing paragraphs.
Keyword Extraction. The goal of the keyword
extraction phase is to find keywords that describe a
ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy
control and its usability well. In our implementation,
the keyword extraction for controls is based on the
control title, the control text as well as the discussion
of each control. The keyword extraction for the IT-
assets is based on the title as well as the properties of
each asset.
We analyzed five different libraries for the key-
word extraction. These are KeyBERT (KeyBERT,
2023), RAKE (rake nltk, 2023), spaCy (spacy, 2023),
Textrazor (textrazor, 2023), and Yake (yake, 2023).
Our evaluation showed that in our scenario Yake per-
forms best overall in the extraction of keywords.
Clustering. The second step in the analysis pro-
cess is clustering. The idea here is to combine sim-
ilar keywords into a cluster to be able to create tags
from them. As input, the cluster function receives all
keywords and the references to the associated secu-
rity controls. The KMeans algorithm was selected for
clustering. This is because it gives good results, per-
forms well, and is one of the most used algorithms for
clustering. Furthermore, with KMeans, it is also pos-
sible to cluster words or sentences. The cluster size
was set to 20% for the (control) tags and also 20% for
the property tags. In addition, the tolerance value for
cluster cleaning was set relatively high at 600% for
the keywords and 800% for the controls in order to
eliminate only the outliers and not catch too many of
the somewhat larger clusters
Topic Modeling. With the help of topic modeling,
a descriptive term should be found for the clusters. In
the case of topic modeling, the LDA (Blei et al., 2003)
algorithm can be used, which searches for a suitable
topic for a given text.
4.2 Implementation of
Description-Based Approach
In addition to the tag-based approach we evaluated the
description based approach shown in Figure 2b. We
used the well established TF-IDF (Rajaraman et al.,
2014) value to analyze the descriptions of the con-
trols and the IT-assets. Using the TfidfVectorizer from
the Sklearn (scikit, 2023) library, a vector is created
which contains the TF-IDF value for each word of a
description. If two descriptions have the same words,
the vectors have the same value at this point and are
therefore similar to a certain degree. In order to re-
ceive a unique value for the similarity, the Cosine
Similarity between the vectors is computed. The re-
sulting value is used to determine whether a control
matches a given asset.
4.3 Evaluation
We evaluated the implemented algorithms on the
NIST security controls and a selection of 200 IT-
assets from our organizations IT-asset inventory.
The following section presents the results of the
implementation tests. It is divided into two sections,
where on the one hand, the results with the tag-based
approach are presented and, on the other hand, the
results with the description-based approach.
Tag-Based Matching. Table 1 shows general
statistics obtained with the tag-based approach de-
scribed in Section 4.1. A total of 323 out of 1235
unique controls were recognized and assigned to the
captured assets. Some of the controls have been as-
signed to multiple assets. Looking at the total number
of matches, the number of controls matched increases
to 5599. This means that, on average, each control
that was matched was assigned about 17 times, and an
asset has an average of 28 controls assigned to it. Of
the 200 assets, 178 have received at least one control,
which means no controls were assigned to 22 assets.
Table 1: Mapping based on tags.
Description Value
Total matched unique controls 323
Total matched controls 5599
Number of assets with controls 178
Number of assets without controls 22
A manual verification was also performed to
check if the assigned controls matched the assets.
Since the amount of data is tremendous, we only eval-
uated 10 randomly selected assets. Table 2 shows the
results of this verification. A total of 413 assignments
were verified. Of these 413 assignments, 79 were
identified as incorrect. This means that the assigned
control doesn’t match the asset. Thus, 334 assign-
ments were correct in this subset. Thus, the applica-
tion executed about 80% of the assignments correctly
in the analysis with the tag-based approach.
Description-Based Matching. Table 3 shows
an overview of the data from the tests with the
description-based approach.
A total of 370 unique controls were matched. This
represents a share of 28% of the total of 1235 avail-
able controls. The total number of matches is 1981.
On average, each control is assigned to 6 different as-
sets. 159 assets have received at least one control. So
41 assets have not been assigned a single control.
We also performed the manual analysis shown in
Table 4. For the description-based matching, 10 as-
sets were selected and manually checked. As far
as possible, the same assets were selected as for the
tag-based matching in order to obtain a possibility of
Towards Automated Information Security Governance
Table 2: Manual Evaluation of Tag Based Mapping.
Asset Total
Google Drive [Web] 111 26
Cisco AnyConnect VPN
29 3
Microsoft Teams 7 0
Microsoft OneDrive for
Business 2016
120 32
RV-APP-T-202 11 2
Citrix ICA Client 27 0
Microsoft Local Adminis-
trator Password Solution 6
35 5
Microsoft Windows 10
10 0
Microsoft SharePoint On-
line [Web]
29 0
VideoLAN VLC Media
34 11
Total 413 79
comparison. If no control was assigned to an asset, it
was replaced by another, random one. In total, 137 as-
signments were recorded. Of these, 22 were identified
as incorrect. In total, 115 assignments were classified
as correct, which corresponds to a share of 83%.
Table 3: Mapping based on description.
Description Value
Total matched unique controls 370
Total matched controls 2488
Number of assets with controls 159
Number of assets without controls 41
Comparison of Tag-Based and Description-
Based Approach. To assess the quality of the results,
we compared the tag-based and the description-based
approach. As shown in Table 1 and Table 3 many of
the controls identified by the tag-based matching were
not found with description-based matching. With a
total of 2488 matches, the description-based match-
ing generally only identified half as many controls as
the tag-based approach. This circumstance is also re-
flected in the detailed analysis, where many controls
that should be matched are not identified. However, if
a suitable control is found, this assignment is usually
correct, as Table 4 shows. On the other hand, tag-
based matching was also unable to find some controls
that would actually be relevant. For example, for the
asset ”Cisco AnyConnect VPN Client”, the ”Remote
Access” control was only assigned by description-
based matching, but not by tag-based matching. Both,
description-based matching and tag-based matching,
Table 4: Manual Evaluation of Description Based Mapping.
Asset Total
Google Drive [Web] 3 0
Cisco AnyConnect VPN
32 2
Google Gmail [Web] 60 10
Microsoft OneDrive for
Business 2016
3 0
SRV-APP-T-202 5 0
Citrix ICA Client 8 0
Microsoft Local Adminis-
trator Password Solution 6
3 0
Microsoft Windows 10
2 0
VideoLAN VLC Media
19 7
Microsoft Windows Media
Player 12
12 3
Total 137 22
can identify some correct controls that are not found
by the other algorithm. Therefore, further analysis
should be performed to identify the number of appli-
cable controls that were not identified by either of the
Additionally, both methods are limited by the fact
that only controls that are directly relevant to IT-assets
are matched. But the control libraries contain addi-
tional controls that are not applicable to IT-Assets.
Examples for such controls are controls that mandate
processes or concepts. Since those controls are not
bound to an IT-assets, they would not be selected by
a corresponding algorithm. Therefore, the asset based
approach has to be combined with further approaches
such as an approach where controls are mapped to en-
terprise roles such as network administrator, HR, or
CISO or a questionnaire that covers important gaps.
4.4 Discussion of NLP Based Control
We showed that NLP can be used for the identification
of security controls that are relevant to IT-assets. In
this section we highlight some of the challenges.
No Ground Truth. As we did not have a known,
verified mapping of security controls to IT-assets we
had to perform the validation manually. This valida-
tion is time consuming and error prone and to a certain
degree subjective. A validated ground truth would al-
low for a better evaluation of the results.
Parameter Selection. The quality of the results
heavily depends on the chosen algorithms and their
ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy
configurations. If a ground truth would be available,
several different parameters could be tested against
that ground truth and the best parameters could be
chosen. Without that ground truth the parameter se-
lection was performed manually.
Short Asset Description. While the security con-
trol descriptions contain an adequate amount of text
to be used with NLP, this is not the case for the as-
set description in a traditional asset inventory. Here
the asset is only described with a few key words. To
reach a higher quality in the selection of appropriate
security controls, a more detailed description, includ-
ing the value the asset presents to the organization,
would be desirable.
In addition to NLP mechanisms, we could also
think of using different approaches for the selection
of the relevant security controls. Some possibilities
include: Deep Learning. This would require a data
set containing assets that already have assigned se-
curity controls. This data could then be used to cre-
ate a neural network that establishes a relationship
between certain assets and the controls. However, a
large amount of data is required for this model. Fur-
thermore, it is questionable how large the influence of
subjectivity and the variation of asset specifications is
on the quality of the model. The work of (Bettaieb
et al., 2020) goes in this direction. Based on histori-
cal data, they try to find the appropriate controls for a
new system using machine learning. The researchers
achieved a precision value of 93%. However, the as-
signments are based on security requirements, which
must first be determined.
Linguistic Rules. Another approach is to focus
more on linguistic rules, as is done in (Li, 2017).
Here, an attempt is made to identify security require-
ments employing linguistic rules and machine learn-
ing. The security requirements are divided into differ-
ent categories such as ”threat-based”, ”asset-based”,
etc., whereby a different structure of the linguistic
rules is defined for each category. This approach
could be used to determine security requirements.
These are then used to identify potentially suitable
controls. Machine learning could also be used for this
step, or the model could be extended with the linguis-
tic rules.
Large Language Models. Instead of using ba-
sic NLP methods pre-trained large language models
could be used. Those have the benefit that we would
not have to perform the parameter selection but that
those are already tuned to match a large set of text.
Role Based Approach Instead of Asset Based Ap-
proach. An approach focusing on roles in a com-
pany, such as network administrator, HR, CISO, etc.
could be used in addition to the approach focusing on
IT-assets. Enterprise roles are generally described in
plain text and contain all the responsibilities of a given
role. The mapping could be performed based on the
role description and the control description. Unlike
the IT-asset based mechanism, this approach is appli-
cable to all controls, since each control has to have
someone that is responsible for its implementation. It
would also facilitate the overall security governance,
since the responsible role is clearly defined. With a
mapping of roles to actual personnel, it would be pos-
sible to add contact details of a responsible person to
While the last section focused on the selection of ap-
propriate controls, this section focuses on the prior-
itization of controls. The basic assumption is that a
company has only limited resources and cannot im-
plement all relevant controls simultaneously. There-
fore, the controls that bring the most benefit for the
security of the organization should be implemented
first. In this chapter we describe one possibility on
how to perform this prioritization. For this study,
we used the BSI IT-Grundschutzkompendium (BSI,
2023). The IT-Grundschutzkompendium is a collec-
tion of security controls that have a set of attributes
such as a role that is responsible for that security con-
trol or elementary risks that it is mitigating. Those
risks range from natural hazards over misconfigura-
tions to attacks or loss of qualified staff. Each security
control is assigned to one of three levels (basic, stan-
dard, enhanced) which describes for which protection
needs the control is required.
The idea is to evaluate the current threat landscape
and use this input to prioritize the security controls.
Figure 3: Control Prioritization Model.
Towards Automated Information Security Governance
As shown in Figure 3 on the one hand, the threat
landscape influences the risk of a company and on the
other hand the security controls mitigate the elemen-
tary risks. The missing link is between the elementary
risks and the enterprise risks. We closed this link by
combining elementary risks to enterprise risks. The
enterprise risks can now be weighted either manually
or automatically using a combination of threat feeds
and the description available in the digital twin.
5.1 Risk Assessment
In this implementation we choose the manual risk as-
sessment method. The responsible personnel assigns
a risk score between 0 and 100 to each of the enter-
prise risks.
5.2 Control Prioritization
In order to calculate the priority of a control, its asso-
ciated risk score is calculated. As shown in Figure 4,
the risk score of a control is defined as the sum of
the risk scores of all enterprise risks that it mitigates
multiplied by the importance of that control.
Figure 4: Risk Score Calculation.
The importance of a control is deter-
mined by its classification in the BSI IT-
Grundschutzkompendium. The corresponding
values are shown in Table 5.
Table 5: Value for the importance of a security control.
Description Value
Basic 6
Standard 4
Enhanced 2
For example if a security control is associated with
three enterprise risks which have risk score of 24, 42
and 51 and the control is classified as standard, the
risk score is 4 (24 + 42 + 51) = 468.
The risk score is then used to prioritize the secu-
rity controls. The use of the importance ensures that
more basic controls are always prioritized higher than
more advanced controls and the sum of the enterprise
risk scores ensures that controls that mitigate more
likely risks are prioritized higher.
5.3 Risk Assessment
In order to complete the security management cycle,
the current risks resulting from cyber threats should
be assessed. To this end the implementation status
of the controls need to be entered. In our basic im-
plementation, a binary form (implemented/not imple-
mented) is chosen that needs to be specified manu-
ally. More elaborate versions could either assess the
implementation state of a control automatically or of-
fer additional states such as partially implemented or
implemented for a fraction of the IT-assets.
For each enterprise risk it the fraction of mitigat-
ing controls implemented over the totally assigned
controls is calculated. This calculation could be en-
hanced by taking the weights (e.g., the importance of
a control, respectively the risc score of an enterprise
risk) in account.
Figure 5 shows the resulting dashboard.
Figure 5: Dashboard Security Control Prioritization.
We presented an architecture for a tool that could sup-
port small and medium sized organization with their
security maturity. The architecture builds on openly
available data such as established security control cat-
alogues as well as open source threat information
for risk assessment. In order to select the security
controls that are relevant for a given organisation as
much information as possible is collected by combin-
ing openly available data as well as company internal
data such as questionnaires, network scans or existing
assets inventories.
ICISSP 2024 - 10th International Conference on Information Systems Security and Privacy
In the remainder of the paper we evaluated meth-
ods for two aspects of a corresponding tool: control
selection and control prioritization. We showed that
we could automate the selection of controls by NLP
mechanisms. However, future work should analyse
whether the selected controls are complete or whether
some controls were missing, and the number of erro-
neously selected controls should be further reduced.
Additionally, mechanisms should be developed that
are applicable to the controls that are not covered by
the IT-asset based approach.
For the control prioritization we showed that con-
trols can be prioritized based on the currently ob-
served threats. In our implementation we used a man-
ual threat assessment, this work could be continued to
automate the treat assessment when appropriate threat
intelligence information is available.
In order to obtain a tool that covers all aspects
of the proposed architecture, the two elements con-
trol selection and control prioritization need to be in-
tegrated and additional topics need to be addressed.
This includes the development of a digital twin of an
organization that is more specific than an asset inven-
tory, and the development of possibilities that auto-
matically assess whether a control is implemented.
We expect that a tool implementing all building
blocks of the architecture would provide a significant
step forward in supporting small and medium sized
organization with their efforts towards securing their
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