Innovating at Speed While Maintaining Control and Trust
Innovating at Speed While Maintaining Control and Trust
Artificial intelligence is reshaping cybersecurity. It accelerates innovation, automates decisions and creates new opportunities. At the same time, it is changing the pace at which organisations must secure their digital environments.
AI enables businesses to improve productivity, optimise processes and develop new services. The same technologies are also helping threat actors identify vulnerabilities, automate reconnaissance and conduct more sophisticated social engineering.
A divide is therefore emerging between organisations. Some are using AI to create competitive advantage, while others are still developing the governance, security and visibility needed to manage it responsibly.
The challenge is not simply that AI creates new risks. It magnifies existing weaknesses at a speed that traditional operating models may struggle to match. Activities that once took months can increasingly be completed in days or even hours.
The question is no longer whether AI will transform business. It is whether cybersecurity strategies can evolve quickly enough to keep pace.
AI is accelerating both cyber defence and attack capabilities
Artificial intelligence creates significant opportunities for security teams. It can enrich alerts, automate investigations, recognise behavioural patterns and help prioritise security actions.
However, attackers are also using these capabilities. Automation reduces the time and specialist knowledge required for certain activities involved in preparing attacks.
AI-enabled cyber defence
Faster alert analysis, more efficient incident investigation, automation of repetitive tasks and improved identification of vulnerabilities.
AI-enabled attack capabilities
Automated reconnaissance, more efficient vulnerability discovery, advanced social engineering and faster adaptation of attack techniques.
Security must be built into AI from the beginning
Many organisations still manage cybersecurity as a technology function, while treating AI primarily as an innovation initiative. That separation is becoming increasingly difficult to sustain.
AI influences customer services, operational processes, financial reporting, supply chains and strategic decisions. Privacy, security, resilience, governance and trust must therefore be integrated into AI initiatives from the outset.
If these questions cannot be answered quickly and clearly, the organisation may be moving faster than it can effectively govern its AI capabilities.
Identity and access are becoming the new security control plane
The digital enterprise now extends beyond employees and their accounts. Service accounts, APIs, automation platforms, AI assistants, autonomous agents and third-party integrations are expanding rapidly.
Many of these systems already hold privileges comparable to those granted to employees. Organisations therefore need visibility not only into who can access data and applications, but also what non-human identities exist and what actions they can perform.
From incident management to continuous exposure management
Traditional cybersecurity programmes were designed for environments where change occurred more slowly. Annual risk assessments, quarterly reviews and periodic testing may no longer reflect the pace of technological development.
Modern approaches therefore focus on continuously assessing exposure. Organisations need to evaluate critical assets, sensitive data, identities, vulnerabilities, third parties and AI systems.
The objective is not to achieve perfect security. It is to reduce uncertainty and identify potential disruption before it affects business operations.
Use AI extensively. Govern it rigorously.
Some organisations may hesitate to adopt AI because of risk. Others may accelerate without adequate safeguards. Neither approach is sustainable in the long term.
Successful organisations will use AI to improve security operations, automate investigations and support decision-making. At the same time, they will retain human accountability for decisions with significant business consequences.
Clear guardrails
Define thresholds for automation and situations requiring human oversight.
Continuous validation
Monitor, test and evaluate AI-generated outputs and decisions.
Evidence-based trust
Verify security, reliability and alignment with business objectives.
Building resilience for machine-speed failure
Many business continuity and recovery plans were developed for incidents unfolding at a slower pace. A compromised AI model, poisoned dataset, manipulated algorithm or exploited AI agent can create consequences much faster and at greater scale.
Organisations therefore need to test AI failure scenarios, incorporate them into crisis exercises, validate recovery procedures and establish clear escalation paths and accountabilities.
Cyber resilience is no longer simply about restoring systems. It is also about maintaining trust when automated decisions or digital services fail to perform as expected.
The leadership challenge in the age of AI
Technology, controls and platforms will continue to matter. However, leadership will play a decisive role in managing speed, accountability and trust.
Executives and boards need to understand where AI creates value, where it introduces exposure, who owns the associated risks and whether controls are working.
Seven practical steps towards secure AI adoption
The global BDO perspective identifies seven practical moves that can help organisations turn their ambition for secure innovation into measurable actions.
Map the AI-enabled enterprise
Maintain an up-to-date inventory of AI use cases, models, agents, data sources, integrations, vendors and machine identities. Classify each by business impact and exposure.
Outcome: A single view of where AI operates, what it can access and which capabilities matter most.
Name accountable owners
Assign a business, technology, data and risk owner for every material AI use case. Define who approves deployment, accepts risk, monitors performance and can stop the capability.
Outcome: Clear decision rights and escalation paths.
Create responsible speed
Establish a fast lane for lower-risk applications. Apply stronger controls where AI affects customers, employees, critical operations, sensitive data or consequential decisions.
Outcome: Governance that accelerates safe innovation rather than unnecessarily slowing every use case.
Secure identities, data and agents
Apply least privilege, strong authentication, secrets management, data access controls, logging and lifecycle management to human and non-human identities.
Outcome: Every person, service, model and agent has only the access it needs, for as long as it is required.
Modernise defence with AI
Use AI to enrich alerts, improve correlation, accelerate investigation, prioritise exposure and automate repeatable, high-confidence actions. Retain human judgement for material decisions.
Outcome: Faster detection, investigation and containment of cybersecurity incidents.
Test continuously
Validate controls, attack paths, model behaviour, recovery procedures and third-party dependencies. Include AI compromise, data poisoning, agent misuse and automated decision failure in exercises.
Outcome: Evidence that controls and resilience measures operate effectively under realistic pressure.
Measure what matters
Replace activity-heavy reporting with a focused view of material exposure, detection and containment times, privileged access, control effectiveness, resilience, AI exceptions and risk acceptance.
Outcome: Leadership can see whether risks are reducing and whether the organisation can operate with confidence.
Start with what matters most
There is no need to perfect all seven steps before beginning. Start with material AI use cases, critical business services, sensitive data, privileged identities and the most credible attack paths.
Establish a 90-day action plan, identify accountable owners, agree on the evidence and reports leaders will receive, and review progress at a defined cadence.
Competitive advantage comes from governing AI better
Artificial intelligence represents one of the most significant shifts cybersecurity has experienced. It also creates opportunities for new approaches, greater efficiency and stronger resilience.
The future belongs to organisations that can innovate quickly, govern intelligently, adapt continuously and demonstrate that their technologies deserve trust.
The real advantage will not come from adopting AI first, but from governing it better.