Adoption of new technologies can alter an organisation’s attack surface, requiring security controls, testing, and risk management to change.
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Background for this topic.
Adoption is the extent to which people and organizations begin using a security technology, control, policy, or practice and incorporate it into routine work. In cybersecurity, adoption is more than purchasing or deploying a capability: it includes correct configuration, user participation, and continued use. Examples include enabling multifactor authentication, applying security patches, using secure coding practices, and collecting logs from systems that require monitoring.
Adoption matters because uneven or incomplete use leaves exploitable gaps. A partially deployed authentication control may protect some accounts while others remain exposed; delayed patch adoption can leave known vulnerabilities available to attackers; and missing or poorly configured logging can limit detection and investigation. Practitioners therefore assess coverage, exceptions, configuration quality, and whether controls operate as intended. Training, usable workflows, staged rollout, and measured policy compliance can improve adoption without encouraging insecure workarounds or unnecessary collection of personal data.
Netskope's Sanjay Beri on Data Risk, Agent Visibility and Enabling AI SafelyAI adoption has outrun enterprise security, leaving data exposed and controls nonexistent. Sanjay Beri, co-founder and CEO at Netskope, says the answer isn't restriction. It's visibility, context and a culture of enablement.
Morgan Stanley's Meta Marshall on Where AI Will Disrupt Cybersecurity MarketsAI adoption in cybersecurity is still largely consumer-driven, but real growth depends on enterprise deployment. Meta Marshall, managing director at Morgan Stanley, explains what's holding back adoption, where AI can deliver value and which security segments are most defensible.
Keyrock CISO David Cass on Managing Agentic AI Risk in Financial ServicesAs financial institutions accelerate AI adoption, traditional governance models are falling short. David Cass, CISO at Keyrock, explains why organizations must rethink accountability, asset visibility and identity controls to manage emerging risks from LLMs and agentic AI systems.
Arctic Wolf CEO Nick Schneider on How Visibility, Human Oversight Shape AI AdoptionAI adoption is accelerating, but security leaders now demand proof of effectiveness and trust. Arctic Wolf CEO Nick Schneider explains why visibility, data evidence and human oversight are critical to ensure AI delivers reliable outcomes in cybersecurity operations.
Alex Doll of Ten Eleven Ventures on Q-Day Risk ConsiderationsQuantum computing advances push security teams to replace encryption keys faster and adopt quantum-resistant algorithms. Investors and enterprises now treat Q-Day as a near-term risk, forcing changes in key management, PKI and cryptographic standards, says Alex Doll of Ten Eleven Ventures.
7AI's Lior Div on Building Knowledge Graphs, Human Oversight to Drive AI AccuracySecurity teams face an AI reality check as tools require deep organizational context to deliver value. Lior Div, co-founder and CEO of 7AI, explains how knowledge graphs, human oversight and phased adoption can help teams improve accuracy, build trust and scale AI-driven security operations.
Meerah Rajavel of Palo Alto Networks on AI Security, Governance and Use-Case FitAs AI outpaces governance and security frameworks, enterprise leaders face a more pressing question: How can they move fast without losing control? Meerah Rajavel of Palo Alto Networks says organizations need security guardrails, clear use cases and firm limits on probabilistic AI.