Tag: shadow AI

  • Cloud Cybersecurity Risks: Credential Abuse, Misconfigurations, and the Rise of Identity-First Defense

    Cloud Cybersecurity Risks: Credential Abuse, Misconfigurations, and the Rise of Identity-First Defense

    Cloud breaches now often begin with stolen credentials rather than broken infrastructure. Misconfigurations and excessive permissions can turn minor weaknesses into major incidents, while multi-cloud complexity and shadow AI expand the attack surface. Identity-first security and stronger governance are essential to containing these risks.

    Why Cloud Environments Are Harder to Defend

    Every cloud workload, API, and third-party connection is a possible entry point, and each one usually sits with a different team or vendor. That spread has a real cost. IBM’s 2025 Cost of a Data Breach Report, based on 600 breached organizations across 17 industries, found breaches spanning multiple environments cost an average of USD 5.05 million. On-premises breaches, by comparison, cost USD 4.01 million. Complexity is not just a headache for IT teams; it shows up directly on the balance sheet.

    There is a bright spot in the same report: global breach costs fell for the first time in five years, dropping to USD 4.44 million, helped along by faster detection through AI-powered tools. But the gain is not shared evenly. In the United States, average breach costs actually climbed to USD 10.22 million, pushed up by regulatory fines and slower response times.

    Misconfiguration: One Small Gap, One Long Chain

    The 2019 Capital One breach still offers the clearest lesson. An attacker found a server-side request forgery flaw in a web application firewall running on AWS. That flaw let her query the cloud’s metadata service and pull out credentials. Those credentials then opened access to storage buckets holding customer records, exposing data from roughly 106 million people. The breach did not happen because of one mistake; it happened because two mistakes lined up: a vulnerable firewall and a firewall role that carried far more storage access than its job required. Fixing either link would have broken the chain.

    Identity Is the New Front Door

    Authentication and permission are two separate questions, and mixing them up is where most damage starts. A stolen password authenticates just fine every time it is used. The real question is what that login can reach once it is inside. This is why credential theft tops the list of breach vectors and why 88% of attacks on basic web applications involved stolen logins, with human error playing a part in 60% of all breaches. Infostealer malware has made this worse at scale. Verizon’s 2025 Data Breach Investigations Report found company credentials stored in infostealer logs on 30% of corporate devices and 46% of unmanaged ones, and over half of ransomware victims had exposed credentials in those logs before the attack even started.

    Multi-Cloud and Shadow AI: Two Fast-Growing Gaps

    A single security rule, such as keeping production databases private, has to be enforced separately across AWS, Azure, Kubernetes, and a dozen SaaS admin panels. One policy, several enforcement points, and each gap between them is where risk quietly builds up. Third-party access adds to the strain: breaches tied to outside partners doubled in a year, now making up 30% of all cases. Shadow AI is the newest version of this same problem. Staff paste code or client data into unapproved AI tools that lie outside normal data controls. IBM found that one in five studied organizations had a breach tied to shadow AI, adding up to USD 670,000 to the average cost and exposing customer data at a higher rate.

    Detection Is Getting Faster, Just Not Fast Enough

    A single action in a cloud system can touch an identity provider, an API, and a SaaS app, each logging events in its own format. The problem is not a lack of data; it is a lack of connection between the pieces. IBM’s numbers show real progress: breach lifecycles dropped to 241 days, the shortest in nine years, split between 181 days to detect and 60 days to contain. Organizations that take longer than 200 days to contain a breach pay USD 1.14 million more on average than those that move faster.

    Final Thought

    Cloud security has stopped being a story about firewalls and started being a story about access. The organizations closing the gap are not the ones buying the most tools. They are the ones asking a simpler question every day: who can reach what, and should they still be able to?

  • AI Adoption Surges in Indian Enterprises, but Security Lags Behind: Report

    AI Adoption Surges in Indian Enterprises, but Security Lags Behind: Report

    Artificial intelligence is no longer a pilot project in Indian enterprises. According to Proofpoint’s 2026 AI and Human Risk Landscape report, 94% of organizations in India have deployed AI assistants beyond the pilot stage, and 88% are advancing autonomous agents. However, security readiness has not kept pace, with 35% of organizations describing their AI security posture as catching up, inconsistent, or reactive. More than three in five (63%) have already experienced a suspicious or confirmed AI-related incident.

    AI Has Expanded the Attack Surface

    Email remains the most common AI-related threat vector, affecting 70% of organizations. But exposure now extends to SaaS and cloud applications (59%), SMS or text (55%), and collaboration tools like Teams or Slack (54%). Among organizations that experienced an AI-related incident, exposure rises across every channel, including 73% in email and 65% involving SaaS and cloud applications. This expansion means sensitive data can move across multiple environments—email, collaboration platforms, cloud storage, and AI assistants—creating new points of risk.

    Data Security and AI Security Are the Same Problem

    Treating AI security as separate from data security is a common structural error. The earliest challenge was employees using consumer AI tools with sensitive business data: 63% of employees who used AI applications shared confidential company data with personal chatbot accounts. Shadow AI breaches cost an average of $670,000 more than standard incidents, according to IBM’s Cost of a Data Breach Report. The second wave involves enterprise AI platforms like Microsoft Copilot and Salesforce Einstein, where the key question is whether AI tools access only authorized data. The third wave—autonomous agents—adds real-time, agentic activity where agents connect to external tools and MCP servers, making visibility into tool calls and downstream actions critical.

    Gartner projects that by end of 2026, up to 40% of enterprise applications will integrate with AI agents, and by 2028, 25% of all enterprise GenAI applications will experience at least five minor security incidents per year. Only 57% of organizations in India say they are fully prepared to investigate an AI- or agent-related incident, and 47% report difficulty correlating threats across multiple channels.

    Tool Sprawl Is Holding Security Teams Back

    Almost all organizations in India say managing multiple security tools is at least moderately challenging, and more than half describe it as very or extremely difficult. Fragmented security stacks lead to time lost moving between dashboards and reconciling alerts. As AI scales, security architecture becomes a strategic priority. Over the next 12 months, 67% of organizations in India plan to expand AI protections, 71% intend to extend collaboration channels, and 58% expect to move toward a unified platform approach.

    AI adoption in India is not slowing down. Boards and CEOs driving it recognize the competitive cost of falling behind. Security leaders now have an opportunity to enable AI innovation with the visibility to secure, govern, and defend it.

  • AI Security Threats in 2026: What Every Cybersecurity Team Must Know

    AI Security Threats in 2026: What Every Cybersecurity Team Must Know

    As artificial intelligence becomes deeply embedded in enterprise operations, cybercriminals are weaponizing the same technology to launch increasingly sophisticated attacks. By 2026, cybersecurity teams will need to defend not only traditional networks and endpoints but also the AI models, prompts, and autonomous agents that power modern business processes.

    This article outlines the most critical AI-driven security threats on the horizon and offers actionable strategies to build resilient defenses.

    The New AI Threat Landscape

    Unlike conventional cyberattacks, AI-focused threats exploit machine learning models, training data, prompts, APIs, and automated workflows. Security leaders must now treat AI systems as a distinct security perimeter—just as they do cloud workloads or enterprise networks.

    1. Prompt Injection: A Critical Enterprise Risk

    Prompt injection has emerged as one of the most severe threats for organizations using large language model (LLM) applications. Attackers manipulate how AI systems are instructed, bypassing security controls, exfiltrating sensitive data, or triggering unauthorized actions. When AI agents have access to email, databases, or cloud storage, a malicious prompt hidden in a document or webpage can compromise an entire environment. Every external input should be treated as untrusted.

    2. AI-Generated Phishing and Deepfakes

    Generative AI has supercharged phishing. Attackers now create grammatically flawless emails, mimic individual writing styles, and produce convincing voice and video deepfakes in minutes. Business Email Compromise (BEC) attacks have evolved into deepfake video calls impersonating executives to authorize fraudulent transactions. Relying solely on employee awareness is no longer enough; organizations must deploy AI-based detection solutions.

    3. Data and Model Manipulation

    AI algorithms themselves are attractive targets. Data poisoning injects false information during training, causing biased or incorrect outputs. Adversarial attacks modify model behavior using carefully crafted inputs, while model theft attempts to reverse-engineer proprietary algorithms. These risks are especially acute in healthcare, finance, and critical infrastructure sectors.

    4. AI Agents: New Attack Surfaces

    Autonomous AI agents that interact with APIs, databases, and enterprise applications introduce unprecedented security challenges. Unlike simple chatbots, agents can execute workflows and make decisions without human oversight. Attack vectors include prompt injection, plugin exploits, and insecure integrations. Security teams must enforce identity management, least-privilege access, runtime monitoring, and comprehensive logging. Gartner identifies agentic AI governance as a top cybersecurity concern.

    5. Shadow AI

    Employees often use unauthorized AI tools, exposing sensitive data to public AI platforms. Without proper governance, this “shadow AI” can lead to data leaks and compliance violations. Organizations need policies, monitoring, and employee education to manage unsanctioned AI usage.

    Building AI-Ready Cybersecurity Defenses

    Preparing for AI-native attacks requires more than adding new security tools. Key steps include:

    • Establishing AI governance policies and ethical guidelines.
    • Conducting regular red-team testing of AI systems.
    • Monitoring model behavior in production for anomalies.
    • Educating employees about AI-enabled attack methods.
    • Integrating AI incident response plans into overall cyber resilience strategies.

    Why This Matters

    The future of cybersecurity is AI-aware. As organizations accelerate AI adoption in customer support, software development, and business optimization, threats targeting AI systems will multiply. Organizations that recognize AI as both a powerful tool and a new security frontier will be best positioned to reduce risks and harness AI safely.