Cybersecurity Expertise

Securing automotive software from threats

Vehicle connectivity is rapidly expanding, creating new functions but also significant cybersecurity vulnerabilities. Stricter regulations like ISO 21434 and the United Nations Economic Commission for Europe (UNECE) WP.29 are mandating a certified Cybersecurity Management System (CSMS) and Software Update Management System (SUMS) for OEMs, ensuring secure design from components to the vehicle level.
This is not just about compliance; it’s about protecting brand reputation, ensuring customer safety, and enabling new business models. Emerging trends include advanced threat analysis, AI–powered security, and continuous monitoring, to ensure security of automotive software.

How Acsia Can Help?

  • Threat Analysis and Rish Assessment (TARA)
  • Tailored security strategies & roadmaps
  • CSMS compliance guidance (UNECE WP.29 R155/R156)
  • Certification support (ISO 21434)
  • Secure specific component connected to invehicle network
  • Security measures (secure boot, TLS/SSL, encryption)
  • Secure OTA update mechanisms
  • HSM integration
  • Secure external communication interfaces
  • Secure SDLC implementation
  • Security features in ECUs
  • Secure boot for ECUs
  • Secure invehicle communication
  • IDPS for invehicle networks
  • Access control mechanisms
  • Vulnerability assessment of vehicle systems
  • Penetration testing (pen testing
  • Security analysis of connected components
  • Resilience testing against threats
  • Remediation recommendations
  • Security test plan development
  • Fuzz testing
  • Security regression testing
  • Compliance validation
  • Specialized testing tools & methodologies
  • Securing cloud platforms & backend systems
  • Secure authentication & authorization
  • Data protection (encryption, access controls)
  • API security
  • Security updates & patches

Project Highlights

Implemented 280+ cybersecurity requirements for both engineering and testing domains.

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Implemented cybersecurity and feature functionalities, developed and verified source code, conducted integration and testing, and completed design and documentation in alignment with system requirements.

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TARA and vulnerability management for a cockpit solution where Acsia provided the customer TLS connection manager and designed secure access end-to-end architecture.

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Why Acsia?
Complete Ownership
From analysis to continuous monitoring and incident management.
Rapid Compliance
Proven track record in achieving ISO 21434 compliance quickly.
AI & ML Expertise
Leveraging advanced analytics for enhanced security.
Test Bench Availability
Access to a cybersecurity-capable automotive test bench.
IP Integration
Cybersecurity modules available within their AUTOSAR stack.
Strategic Partnerships
Collaboration with specialist security providers.
From analysis to continuous monitoring and incident management.
Proven track record in achieving ISO 21434 compliance quickly.
Leveraging advanced analytics for enhanced security.
Access to a cybersecurity-capable automotive test bench.
Cybersecurity modules available within their AUTOSAR stack.
Collaboration with specialist security providers.
What’s In It For You

Acsia provides tailored ISO 21434 compliance and certification programs, ensuring that OEM processes and development methodologies meet the specific requirements of the standard. This approach helps OEMs navigate the complexities of compliance efficiently and effectively, minimizing the burden on internal resources.

Acsia’s expertise ensures adherence to critical global regulations like UNECE WP.29, which is increasingly becoming a mandatory requirement for selling vehicles in key markets. By leveraging Acsia’s knowledge, OEMs can confidently meet these regulatory demands, avoiding potential delays and ensuring market access.

Integrating cybersecurity from the initial development stages is crucial for building secure vehicles. Acsia helps OEMs embed security considerations early in the design process, preventing costly redesigns and ensuring that security is a fundamental aspect of the vehicle architecture rather than an afterthought.

Acsia offers comprehensive testing capabilities, including advanced penetration testing and automated security assessments, ensuring thorough validation of security measures. This rigorous testing helps identify and address vulnerabilities early, minimizing risks and improving the overall security posture of the vehicle.

Acsia addresses OEM safety requirements holistically, providing support across the entire lifecycle of vehicle development, from concept to postproduction monitoring. This comprehensive approach ensures that security is considered at every stage, providing a robust defence against evolving cyber threats.

Acsia leverages its experience across various industries to bring valuable cybersecurity insights and best practices to the automotive sector. This crosspollination of knowledge allows OEMs to benefit from lessons learned in other domains, enhancing their cybersecurity strategies and resilience.

Frequently Asked Questions

With the rise of connected vehicles, cybersecurity is crucial to protect against cyber threats that could put vehicle safety, personal data, and essential systems at risk. Regulations like ISO 21434 and UNECE WP.29 require strong security measures, ensuring vehicles are designed and maintained with robust protection against cyber risks.

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Modern vehicle software incorporates multiple layers of security, including secure boot mechanisms, encryption (TLS/SSL), secure over-the-air (OTA) updates, hardware security modules (HSM), intrusion detection/prevention systems (IDS/IPS), and rigorous penetration testing to identify and mitigate vulnerabilities.

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Acsia offers comprehensive automotive cybersecurity solutions covering consulting, architecture, and validation. The company helps OEMs and Tier-1s achieve CSMS compliance (UNECE R155/R156), implement secure SDLC practices, and design robust vehicle architectures with secure boot, encryption, HSM, and OTA protection. The offerings also include ECU-level security, cloud/backend protection, and rigorous testing like fuzzing, TARA, and penetration testing.

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Acsia provides end-to-end cybersecurity solutions, including identifying potential risks, secure software design, vulnerability management, penetration testing, and handling and recovering from cyber incidents. The company’s expertise ensures OEMs achieve ISO 21434 and UNECE WP.29 compliance efficiently while integrating security seamlessly into their vehicle development lifecycle.

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Acsia secures ECUs through robust firmware protection, secure boot mechanisms, encrypted firmware updates, intrusion detection systems, and limiting access to authorized users only. The company also performs security validation tests, such as fuzz testing and penetration testing, to safeguard in-vehicle communication networks.

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  • Building one of the world’s first Android-based Rear Seat Entertainment (RSE) system for a German OEM through a leading Japanese Tier-1.
  • SW Design, Development and Testing of a Smart Gateway for a US OEM.
  • Cybersecurity Support for a Hypervisor-based Cockpit Solution for a Dutch OEM.
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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
  • Provide actionable insights that developers can use immediately.

Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
AH2025/PS03 | AI/ML

Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
  • Resource allocation map showing workload distribution across the team.
  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
AH2025/PS01 | AI/ML

Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.
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